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Decidim describes itself as a "technopolitical network for participatory democracy". It combines a free and open-source software (FOSS) software package together with a participatory political project and an organising community, "Metadecidim". Decidim participants describe the software, political and organising components as "technical", "political" and "technopolitical" levels, respectively. Decidim's aims can be seen as promoting the right to the city, as proposed by Henri Lefebvre. As of 2023, Decidim instances were actively in use for participatory decision-making in municipal and regional governments and by citizens' associations in Spain, Switzerland and elsewhere in Europe. Studies of the use of Decidim found that it was effective in some cases, while in one case implemented top-down in Lucerne, it strengthened the digital divide.
== Creation ==
A server called "Decidim" was created by the 15M anti-austerity movement in Spain in 2016, running a fork of the "Consul" software, when a political party derived from the protest movement obtained political power. In early 2017, the server was switched to a similarly inspired, but new software project, Decidim, completely rewritten, aiming to be more modular and convenient for development by a wide community.
Founded in Barcelona, the name "Decidim" comes from a Catalan word meaning "let's decide" or "we decide". Privacy and citizen-ownership of data was a key motivator to improve discussion and engagement that had previously been happening on social media.
== Software ==
Decidim uses Ruby on Rails. As of 2022, the software defines two structures: "participatory spaces" and "participatory components". The participatory spaces (six as of early 2024) include "processes" (such as a participatory budget), "assemblies" (such as a citizens' association website), "conferences/meetings", "initiatives", and "consultations (voting/elections)". The participatory components (twelve as of early 2024) range from "comments", "proposals", "amendments", "votes" through to "accountability". Together these allow a wide flexibility in creating specific space–component combinations. The "accountability" component is used to monitor whether and how a project is executed.
As of 2022, three user levels are defined: general visitors with view-only access; registered users who have several participation rights; and verified users who can participate in decision-making. Users may be individuals or represent associations or working groups within an organisation. Users with special privileges are called "administrators", "moderators" and "collaborators".
As of 2022, four versions of Decidim had been released.
The Decidim software development strategy is intended to be modular and scalable. As FOSS, the software is intended to encourage both citizen and government interaction with each other and with decision-making power over the software itself, aiming at high levels of traceability and transparency.
Decidim software provides an application programming interface (API) for command line access.
== Technopolitical project ==
In the spirit of the Decidim software being free and open-source software (FOSS), a community of software developers, social activists, software consultancies, researchers, and administrative staff from municipal governments called Metadecidim was created for discussing and analysing Decidim experience and development. Metadecidim is seen as an intermediary component between the political level of Decidim, implemented on servers such as Barcelona Decidim, and the technical level of hosting the software source code and bug reporting structures. As of June 2023, Metadecidim had about 5000 registered participants.
The Decidim community has a text called the Decidim Social Contract (DSC) that defines six guidelines. The DSC defines the free software licences that may be used for Decidim software; it defines requirements of transparency, traceability and integrity of content hosted by Decidim software; a goal of equal access to all users and democratic quality parameters to measure progress towards equality; data privacy; and it requires inter-institutional cooperation of institutions implementing instances of the software, in order to encourage further development. The free software licensing is the GNU Affero General Public License (AGPL) version 3 for code; the CC BY-SA licence is used for content; and "data" is published under the Open Database License.
Philosophically, the aims of Decidim can be seen as promoting the right to the city, as proposed by Henri Lefebvre. Metadecidim's self-description as "technopolitical" is seen as implying that the political implications of designs and choices of software are seen as significant, in opposition to the view that software is "value neutral and objective". Metadecidim sees Decidim as a "recursively democratic infrastructure", in the sense that the software, political and server infrastructure is "both used and democratised by its community, the Metadecidim community".
Decidim proponents see the combination of online and offline participation as fundamental: "From its very conception until today, a distinguishing feature of Decidim over other kinds of participatory democracy software ... was that of connecting digital processes directly with public meetings and vice versa."
Organisationally, the community formally established Decidim Association in 2019 and City Council of Barcelona gave control of the Decidim trademark and code base to Decidim Association. The effect was to combine public funds with citizens' association control of decision-making.
== Use of Decidim ==
In 2022, Borge and colleagues estimated that there were 311 instances running Decidim in Spain and in 19 other countries; while Borges and colleagues estimated that there were Decidim instances run by 80 local and regional governments and 40 citizens' associations in Spain and elsewhere. In 2023, Suter and colleagues cited Decidim's own estimate of 400 city and regional governments and civil society institutions using Decidim. The Open University of Catalonia, the University of Bordeaux and the University of Caen Normandy ran Decidim instances.

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=== Decidim Barcelona ===
A Decidim server was run by the City Council of Barcelona for a two-month trial prior to 2017, in which 40,000 citizens discussed their own proposals and proposals made by the council. The Decidim software allowed threaded discussion, labelling whether the initial comment on a proposal was negative, neutral or positive, and notification to participants.
The two-month trial included both online and face-to-face participation. According to Decidim, about 40% of the 39,000 individual participants did so face-to-face, and about 85% of the organisational participants did so face-to-face. There were about 11,000 proposals made on the Decidim server, of which about 8000 were accepted. The execution of the proposals was monitored during the following four years, spending about 90% of the Barcelona City Council's budget for 2016–2019.
=== Zurich and Lucerne ===
In Switzerland, urban development has legal requirements in relation to citizen participation. Use of Decidim in Zurich and Lucerne in 2021 and 2022 was studied by Suter and colleagues, based on documentary evidence, interviews with 15 people in Zurich and 17 in Lucerne ranging from municipality employees through to representatives of neighbourhood associations, and "participatory observations" (informal participatory events observed by the researchers). The researchers found that the effectiveness of Decidim varied significantly between the different cases, and argued that the "full potential" of Decidim had not yet been achieved in Switzerland.
In Wipkingen in Zurich, two local citizens' associations used a server running Decidim to run a participatory budget to spend CHF 40,000. The project, named "Quartieridee", had 99 submissions of proposals and awarded funding to eight proposals. The researchers found that overall implementation was dependent on significant financial resources and citizens' voluntary work; and had difficulties due to the municipality lacking legal procedures for implementing the citizens' chosen projects.
The project was scaled up to the Zurich city level the following year with the name "Stadtidee" and a participatory budget of CHF 540,000. Among the successful projects was a confrontation between a citizens' association, "Linkes Seeufer für Alle" opposed to a Kibag AG in relation to a plot of land owned by Kibag next to Lake Zurich. An effect of the Decidim networking was that citizens legally occupied the plot of land for several days.
In 2021, the LuzernNord area of Lucerne was an area with many migrants and people with low incomes, at risk of gentrification. A top-down use of a Decidim server by the local administration, in which citizens' associations were encouraged to participate, was found by the researchers to strengthen the digital divide rather than overcoming it. Limitation of the language to German and lack of confidence in being able to participate effectively were found to be specific effects opposing the effectiveness of the project.
=== Other municipalities ===
Based on nine in-depth interviews with officials responsible for Decidim, conducted in 2018 in some of the initial municipalities that used Decidim, online interviews in March 2019 with officials from 34 municipalities using Decidim, and data from the Decidim servers, the effectiveness of Decidim in terms of transparency, participation in decision-making, and deliberation (discussion of proposals) was studied by Rosa Borge and colleagues. It was found that the officials saw Decidim's role as primarily promoting transparency and the collecting of citizens' proposals, while having only a modest role in transferring decision-making to citizens and a minor role in encouraging online citizen debate.
Several municipalities' use of Decidim provided their first use of participatory budgeting.
The Borge et al. study also found, consistently with other research, that the participatory aspect of citizens making proposals and participating in decisions was obstructed in some cases by local civil society associations, since direct citizen participation was seen to be in competition with the associations' roles. Several municipal governments worked on the implementation of Decidim together with local associations, adding features to the software such as different weightings for proposals by individuals versus those by associations.
The use of Decidim and participatory processes was found to depend on electoral results in some cases: these ceased in Badalona after Dolors Sabater lost power as Mayor in June 2018.
== Recognition ==
In 2023, the Decidim software was recognised as satisfying the criteria of the Digital Public Goods Alliance as a digital public good that contributes to the United Nations' Sustainable Development Goals.
== See also ==
technological utopianism (Decidim sees itself as opposed to technological utopianism)
Wiki survey
== References ==
== External links ==
Official website

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The Distributed Artificial Intelligence Research Institute (or DAIR Institute) is a research institute founded by Timnit Gebru in December 2021. The institute announced itself as "an independent, community-rooted institute set to counter Big Tech’s pervasive influence on the research, development and deployment of AI."
In February 2023, two other members of Google's Ethical AI research group, researcher Alex Hanna and developer Dylan Baker, left Google to join DAIR.
== References ==
== External links ==
Official website

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Effective accelerationism (e/acc) is a 21st-century ideological movement that advocates for an explicitly pro-technology stance. Its proponents believe that unrestricted technological progress, especially driven by artificial intelligence, is a solution to universal human problems, such as poverty, war, and climate change. They perceive themselves as a counterweight to more cautious views on technological innovation and often label their opponents derogatorily as "doomers" or "decels" (short for decelerationists).
The movement carries utopian undertones and advocates for faster AI progress to ensure human survival and propagate consciousness throughout the universe. Although effective accelerationism has been described as a fringe movement and as cult-like, it has gained mainstream visibility in 2023. A number of high-profile Silicon Valley figures, including investors Marc Andreessen and Garry Tan, explicitly endorsed it by adding "e/acc" to their public social media profiles.
== Etymology and central beliefs ==
Effective accelerationism, a portmanteau of "effective altruism" and "accelerationism", is a fundamentally techno-optimist movement. According to Guillaume Verdon, one of the movement's founders, its aim is for human civilization to "clim[b] the Kardashev gradient", meaning its purpose is for human civilization to rise to next levels on the Kardashev scale by maximizing energy usage.
To achieve this goal, effective accelerationism wants to accelerate technological progress. It is strongly focused on artificial general intelligence (AGI), because it sees AGI as fundamental for climbing the Kardashev scale. The movement therefore advocates for unrestricted development and deployment of artificial intelligence. Regulation of artificial intelligence and government intervention in markets more generally is met with opposition. Many of its proponents have libertarian views and think that AGI will be most aligned if many AGIs compete against each other on the marketplace. The founders of the movement see it as rooted in Jeremy England's theory on the origin of life, which is focused on entropy and thermodynamics. According to them, the universe aims to increase entropy, and life is a way of increasing it. By spreading life throughout the universe and making life use up ever increasing amounts of energy, the universe's purpose would thus be fulfilled.
== History ==
=== Intellectual origins ===
While Nick Land is seen as the intellectual originator of contemporary accelerationism in general, the precise origins of effective accelerationism remain unclear. The earliest known reference to the movement can be traced back to a May 2022 newsletter published by four pseudonymous authors known by their X (formerly Twitter) usernames @BasedBeffJezos, @bayeslord, @zestular and @creatine_cycle.
Effective accelerationism is an extension of the TESCREAL movement, being etymologically derived from Effective Altruism and heavily rooted in the older Silicon Valley subcultures of transhumanism and extropianism (which similarly emphasized the value of progress and resisted efforts to restrain the development of technology), alongside elements of singularitarianism, cosmism, and longtermism. It is also often considered to have emerged at least in part from the work of the Cybernetic Culture Research Unit (of which Nick Land was a leading member, alongside writers such as Mark Fisher and Sadie Plant). It is sometimes compared and contrasted with the work of philosopher Benjamin Bratton on planetary computation.
=== Disclosure of the identity of BasedBeffJezos ===
Forbes disclosed in December 2023 that the @BasedBeffJezos persona is maintained by Guillaume Verdon, a Canadian former Google quantum computing engineer and theoretical physicist. The revelation was supported by a voice analysis conducted by the National Center for Media Forensics of the University of Colorado Denver, which further confirmed the match between Jezos and Verdon. The magazine justified its decision to disclose Verdon's identity on the grounds of it being "in the public interest". On 29 December 2023 Guillaume Verdon was interviewed by Lex Fridman on the Lex Fridman Podcast and introduced as the "creator of the effective accelerationism movement".
=== Second Trump presidency ===
Following Donald Trump's victory in the 2024 U.S. presidential election, several prominent tech industry figures expressed support for positions aligned with effective accelerationism, particularly regarding deregulation and technological advancement. The potential appointment of Elon Musk to government roles focused on auditing federal programs drew support from venture capitalists who anticipated reduced regulatory oversight of the technology sector. Notable tech figures publicly connected these developments to the movement's principles. Aaron Levie, CEO of Box, expressed support for "removing unnecessary red tape and over-regulation", while Mark Pincus, early Facebook investor and Zynga founder, explicitly referenced "effective accelerationism" in his post-election commentary. Venture capitalists viewed the incoming administration as an opportunity to ease regulations that had affected technology mergers and acquisitions during the previous years.
== Relation to other movements ==
=== Traditional accelerationism ===
Traditional accelerationism, as developed by the British philosopher Nick Land, sees the acceleration of technological change as a way to bring about a fundamental transformation of current culture, society, and the political economy. This is done through capitalism, which Land views as "an autonomous force that’s reconfiguring society" that can overcome its limits if intensified. Land's work has also been characterized as concerning "the supposedly inevitable 'disintegration of the human species' when artificial intelligence improves sufficiently." While both concern ideas like a technocapital singularity and AGI progress, effective accelerationism focuses on using AGI for the greatest ethical good for conscious life and civilization (whether human or machine), as well as expanding civilization and maximizing energy usage in order to align with the "will of the universe". Land focuses on capitalist self-optimization as the driver of modernity, progress, and the eroding of existing social orders. Land has expressed support for effective accelerationism, while Thomas Murphy referred to the movement as "Nick Land diluted for LinkedIn".

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=== Effective altruism ===
Effective accelerationism diverges from the principles of effective altruism, which prioritizes using evidence and reasoning to identify the most effective ways to altruistically improve the world. This divergence comes primarily from one of the causes effective altruists focus on – AI existential risk. Effective altruists (particularly longtermists) argue that AI companies should be cautious and strive to develop safe AI systems, as they fear that any misaligned AGI could eventually lead to human extinction. Proponents of effective accelerationism generally consider existential risks from AGI to be negligible, and claim that even if they were not, decentralized free markets would much better mitigate this risk than centralized governmental regulation.
=== Degrowth ===
Effective accelerationism stands in stark contrast with the degrowth movement, sometimes described by it as "decelerationism" or "decels". The degrowth movement advocates for reducing economic activity and consumption to address ecological and social issues. Effective accelerationism on the contrary embraces technological progress, energy consumption and the dynamics of capitalism, rather than advocating for a reduction in economic activity.
== Reception ==
The "Techno-Optimist Manifesto", a 2023 essay by Marc Andreessen, has been described by the Financial Times and the German Süddeutsche Zeitung as espousing the views of effective accelerationism. Mother Jones also characterized it as expressing effective accelerationism and reported that Andressen cited Land's work. David Swan of The Sydney Morning Herald has criticized effective accelerationism due to its opposition to government and industry self-regulation. He argues that "innovations like AI needs thoughtful regulations and guardrails ... to avoid the myriad mistakes Silicon Valley has already made." During the 2023 Reagan National Defense Forum, U.S. Secretary of Commerce Gina Raimondo cautioned against embracing the "move fast and break things" mentality associated with "effective acceleration [sic]". She emphasized the need to exercise caution in dealing with AI, stating "that's too dangerous. You can't break things when you are talking about AI." In a similar vein, Ellen Huet argued on Bloomberg News that some of the ideas of the movement were "deeply unsettling", focusing especially on Guillaume Verdon's "post-humanism" and the view that "natural selection could lead AI to replace us as the dominant species."
== See also ==
Technological utopianism
== References ==
== External links ==
Jezos, Beff; Bayeslord (10 July 2022). "Notes on e/acc principles and tenets". Beff's Newsletter. Substack.

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Engineering ethics is the field concerned with the system of moral principles that apply to the practice of engineering. The field examines and sets the obligations of engineers to society, to their clients, and to the profession. As a scholarly discipline, it is closely related to subjects such as the philosophy of science, the philosophy of engineering, and the ethics of technology. The field of business ethics often overlaps with, and informs, ethical decision making by engineers.
== Background and origins ==
=== Up to the 19th century and growing concerns ===
As engineering rose as a distinct profession during the 19th century, engineers saw themselves as either independent professional practitioners or technical employees of large enterprises. There was considerable tension between the two sides as large industrial employers fought to maintain control of their employees.
In the United States growing professionalism gave rise to the development of four founding engineering societies: the American Society of Civil Engineers (ASCE) (1851), the American Institute of Electrical Engineers (AIEE) (1884), the American Society of Mechanical Engineers (ASME) (1880), and the American Institute of Mining Engineers (AIME) (1871). ASCE and AIEE were more closely identified with the engineer as learned professional, where ASME, to an extent, and AIME almost entirely, identified with the view that the engineer is a technical employee.
Even so, at that time ethics was viewed as a personal rather than a broad professional concern.
=== Turn of the 20th century and turning point ===
When the 19th century drew to a close and the 20th century began, there had been series of significant structural failures, including some spectacular bridge failures, notably the Ashtabula River Railroad Disaster (1876), Tay Bridge Disaster (1879), and the Quebec Bridge collapse (1907). These had a profound effect on engineers and forced the profession to confront shortcomings in technical and construction practice, as well as ethical standards.
One response was the development of formal codes of ethics by three of the four founding engineering societies. AIEE adopted theirs in 1912. ASCE and ASME did so in 1914. AIME did not adopt a code of ethics in its history.
Concerns for professional practice and protecting the public highlighted by these bridge failures, as well as the Boston molasses disaster (1919), provided impetus for another movement that had been underway for some time: to require formal credentials (Professional Engineering licensure in the US) as a requirement to practice. This involves meeting some combination of educational, experience, and testing requirements.
In 1950, the Association of German Engineers developed an oath for all its members, entitled 'The Confession of the Engineers', directly hinting at the role of engineers in the atrocities committed during World War II.
Over the following decades most American states and Canadian provinces either required engineers to be licensed, or passed special legislation reserving title rights to organization of professional engineers. The Canadian model is to require all persons working in fields of engineering that posed a risk to life, health, property, the public welfare and the environment to be licensed, and all provinces required licensing by the 1950s.
The US model has generally been only to require the practicing engineers offering engineering services that impact the public welfare, safety, safeguarding of life, health, or property to be licensed, while engineers working in private industry without a direct offering of engineering services to the public or other businesses, education, and government need not be licensed. This has perpetuated the split between professional engineers and those in private industry. Professional societies have adopted generally uniform codes of ethics.
=== Recent developments ===
Efforts to promote ethical practice continue. In addition to the professional societies and chartering organizations efforts with their members, the Canadian Iron Ring and American Order of the Engineer trace their roots to the 1907 Quebec Bridge collapse. Both require members to swear an oath to uphold ethical practice and wear a symbolic ring as a reminder.
In the United States, the National Society of Professional Engineers released in 1946 its Canons of Ethics for Engineers and Rules of Professional Conduct, which evolved to the current Code of Ethics, adopted in 1964. These requests ultimately led to the creation of the Board of Ethical Review in 1954. Ethics cases rarely have easy answers, but the BER's nearly 500 advisory opinions have helped bring clarity to the ethical issues engineers face daily.
Currently, bribery and political corruption is being addressed very directly by several professional societies and business groups around the world. However, new issues have arisen, such as offshoring, sustainable development, and environmental protection, that the profession is having to consider and address.
== General principles ==
Codes of engineering ethics identify a specific precedence with respect to the engineer's consideration for the public, clients, employers, and the profession.
Many engineering professional societies have prepared codes of ethics. Some date to the early decades of the twentieth century. These have been incorporated to a greater or lesser degree into the regulatory laws of several jurisdictions. While these statements of general principles served as a guide, engineers still require sound judgment to interpret how the code would apply to specific circumstances.
The general principles of the codes of ethics are largely similar across the various engineering societies and chartering authorities of the world, which further extend the code and publish specific guidance. The following is an example from the American Society of Civil Engineers:

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Engineers shall hold paramount the safety, health and welfare of the public and shall strive to comply with the principles of sustainable development in the performance of their professional duties.
Engineers shall perform services only in areas of their competence.
Engineers shall issue public statements only in an objective and truthful manner.
Engineers shall act in professional matters for each employer or client as faithful agents or trustees, and shall avoid conflicts of interest.
Engineers shall build their professional reputation on the merit of their services and shall not compete unfairly with others.
Engineers shall act in such a manner as to uphold and enhance the honor, integrity, and dignity of the engineering profession and shall act with zero-tolerance for bribery, fraud, and corruption.
Engineers shall continue their professional development throughout their careers, and shall provide opportunities for the professional development of those engineers under their supervision.
Engineers shall, in all matters related to their profession, treat all persons fairly and encourage equitable participation without regard to gender or gender identity, race, national origin, ethnicity, religion, age, sexual orientation, disability, political affiliation, or family, marital, or economic status.
In 1990, EPFL students elaborated the Archimedean Oath, which is an ethical code of practice for engineers and technicians, similar to the Hippocratic Oath used in the medical world.
=== Obligation to society ===
The paramount value recognized by engineers is the safety and welfare of the public. As demonstrated by the following selected excerpts, this is the case for professional engineering organizations in nearly every jurisdiction and engineering discipline:
Institute of Electrical and Electronics Engineers: "We, the members of the IEEE, … do hereby commit ourselves to the highest ethical and professional conduct and agree: 1. to accept responsibility in making decisions consistent with the safety, health and welfare of the public, and to disclose promptly factors that might endanger the public or the environment;"
Institution of Civil Engineers: "Members of the ICE should always be aware of their overriding responsibility to the public good. A member’s obligations to the client can never override this, and members of the ICE should not enter undertakings which compromise this responsibility. The ‘public good’ encompasses care and respect for the environment, and for humanity's cultural, historical and archaeological heritage, as well as the primary responsibility members have to protect the health and well-being of present and future generations."
Professional Engineers Ontario: "A practitioner shall, regard the practitioner's duty to public welfare as paramount."
National Society of Professional Engineers: "Engineers, in the fulfillment of their professional duties, shall: Hold paramount the safety, health, and welfare of the public."
American Society of Mechanical Engineers: "Engineers shall hold paramount the safety, health and welfare of the public in the performance of their professional duties."
Institute of Industrial Engineers: "Engineers uphold and advance the integrity, honor and dignity of the engineering profession by: 2. Being honest and impartial, and serving with fidelity the public, their employers and clients."
American Institute of Chemical Engineers: "To achieve these goals, members shall hold paramount the safety, health and welfare of the public and protect the environment in performance of their professional duties."
American Nuclear Society: "ANS members uphold and advance the integrity and honor of their professions by using their knowledge and skill for the enhancement of human welfare and the environment; being honest and impartial; serving with fidelity the public, their employers, and their clients; and striving to continuously improve the competence and prestige of their various professions."
Society of Fire Protection Engineers: "In the practice of their profession, fire protection engineers must maintain and constantly improve their competence and perform under a standard of professional behavior which requires adherence to the highest principles of ethical conduct with balanced regard for the interests of the public, clients, employers, colleagues, and the profession."
Responsibility of engineers
The engineers recognize that the greatest merit is the work and exercise their profession committed to serving society, attending to the welfare and progress of the majority.
By transforming nature for the benefit of mankind, engineers must increase their awareness of the world as the abode of humanity, their interest in the universe as a guarantee of overcoming their spirit, and knowledge of reality to make the world fairer and happier.
The engineer should reject any paper that is intended to harm the general interest, thus avoiding a situation that might be hazardous or threatening to the environment, life, health, or other rights of human beings.
It is an inescapable duty of the engineer to uphold the prestige of the profession, to ensure its proper discharge, and to maintain a professional demeanor rooted in ability, honesty, fortitude, temperance, magnanimity, modesty, honesty, and justice; with the consciousness of individual well-being subordinate to the social good.
The engineers and their employers must ensure the continuous improvement of their knowledge, particularly of their profession, disseminate their knowledge, share their experience, provide opportunities for education and training of workers, provide recognition, moral and material support to the schools where they studied, thus returning the benefits and opportunities they and their employers have received.
It is the responsibility of the engineers to carry out their work efficiently and to support the law. In particular, they must ensure compliance with the standards of worker protection as provided by the law.
As professionals, the engineers are expected to commit themselves to high standards of conduct (NSPE). [1] 11/27/11
=== Duty to Report (Whistleblowing) ===

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A basic ethical dilemma is that an engineer has the duty to report to the appropriate authority a possible risk to others from a client or employer failing to follow the engineer's directions. According to first principles, this duty overrides the duty to a client and/or employer. An engineer may be disciplined, or have their license revoked, even if the failure to report such a danger does not result in the loss of life or health.
If an engineer is overruled by a non-technical authority or a technical authority they must inform the authority, in writing, the reasons for their advice and the consequences of the deviation from the advice.
In many cases, this duty can be discharged by advising the client of the consequences in a forthright matter, and ensuring the client takes the engineer's advice. In very rare cases, where even a governmental authority may not take appropriate action, the engineer can only discharge the duty by making the situation public. As a result, whistleblowing by professional engineers is not an unusual event, and courts have often sided with engineers in such cases, overruling duties to employers and confidentiality considerations that otherwise would have prevented the engineer from speaking out.
=== Conduct ===
There are several other ethical issues that engineers may face. Some have to do with technical practice, but many others have to do with broader considerations of business conduct. These include:
Relationships with clients, consultants, competitors, and contractors
Ensuring legal compliance by clients, client's contractors, and others
Conflict of interest
Bribery and kickbacks, which also may include:
Gifts, meals, services, and entertainment
Treatment of confidential or proprietary information
Consideration of the employer's assets
Outside employment/activities (Moonlighting)
Some engineering societies are addressing environmental protection as a stand-alone question of ethics.
== Case studies and key individuals ==
Petroski notes that most engineering failures are much more involved than simple technical mis-calculations and involve the failure of the design process or management culture. However, not all engineering failures involve ethical issues. The infamous collapse of the first Tacoma Narrows Bridge, and the losses of the Mars Polar Lander and Mars Climate Orbiter were technical and design process failures. Nor are all engineering ethics issues necessary engineering failures per se - Northwestern University instructor Sheldon Epstein cited The Holocaust as an example of a breach in engineering ethics despite (and because of) the engineers' creations being successful at carrying out the Nazis' mission of genocide. There is the ethical issue of whether engineers consider vulnerability to hostile intent — such as attacks on governmental buildings or industrial sites — with the same rigor as they consider other universal risks, regardless of project specifications. Lysenkoism is a specific form of ethical failure that occurs when engineers (or scientists) allow political agendas to take precedence over professional ethics.
These episodes of engineering failure include ethical as well as technical issues:
Titan submersible implosion (2023)
General Motors ignition switch recalls (2014)
Deepwater Horizon oil spill (2010)
Space Shuttle Columbia disaster (2003)
Space Shuttle Challenger disaster (1986)
Therac-25 accidents (1985 to 1987)
Chernobyl disaster (1986)
Bhopal disaster (1984)
Kansas City Hyatt Regency walkway collapse (1981)
Love Canal (1980), Lois Gibbs
Three Mile Island accident (1979)
Citigroup Center (1978),
Ford Pinto safety problems (1970s)
Minamata disease (1908–1973)
Aberfan disaster (1966)
Chevrolet Corvair safety problems (1960s), Ralph Nader, and Unsafe at Any Speed
Boston molasses disaster (1919)
Quebec Bridge collapse (1907), Theodore Cooper
Johnstown Flood (1889), South Fork Fishing and Hunting Club
Tay Bridge Disaster (1879), Thomas Bouch, William Henry Barlow, and William Yolland
Ashtabula River Railroad Disaster (1876), Amasa Stone
== Notes ==
== References ==
American Society of Civil Engineers (2010) [1914]. Code of Ethics. Reston, Virginia, USA: ASCE Press. Archived from the original on 2011-02-14. Retrieved 2011-12-07.
American Society of Civil Engineers (2000). Ethics Guidelines for Professional Conduct for Civil Engineers (PDF). Reston, Virginia, USA: ASCE Press. Archived from the original (PDF) on 2014-10-21. Retrieved 2013-11-30.
Institution of Civil Engineers (2004). Royal Charter, By-laws, Regulations and Rules. Archived from the original on 2013-12-03. Retrieved 2006-10-20.
Layton, Edwin (1986). The Revolt of the Engineers: Social Responsibility and the American Engineering Profession. Baltimore, Maryland, USA: The Johns Hopkins University Press. ISBN 0-8018-3287-X.
Petroski, Henry (1985). To Engineer is Human: the Role of Failure in Successful Design. St Martins Press. ISBN 0-312-80680-9.
National Society of Professional Engineers (2007) [1964]. Code of Ethics (PDF). Alexandria, Virginia, USA: NSPE. Archived from the original (PDF) on 2008-12-02. Retrieved 2006-10-20.
== Further reading ==
Alford, C.F. (2002). Whistleblowers: Broken Lives and Organizational Power, Cornell University Press. ISBN 978-0801487804, 192 pp.
Fleddermann, C.B. (2011). Engineering Ethics, Prentice Hall, 4th edition. ISBN 978-0132145213, 192pp.
Glazer, M.P. (1991).Whistleblower, New York, NY: Basic Books. ISBN 978-0465091744, 306pp.
Harris, C.E., M.S. Pritchard, and M.J. Rabins (2008).Engineering Ethics: Concept and Cases, Wadsworth Publishing, 4th edition. ISBN 978-0495502791, 332 pp.
Peterson, Martin (2020). Ethics for Engineers, Oxford University Press. ISBN 9780190609191, 256 pp.
Huesemann, Michael H., and Joyce A. Huesemann (2011). Technofix: Why Technology Won’t Save Us or the Environment, Chapter 14, “Critical Science and Social Responsibility”, New Society Publishers, Gabriola Island, British Columbia, Canada, ISBN 0865717044, 464 pp.
Martin, M.W., and R. Schinzinger (2004). Ethics in Engineering, McGraw-Hill, 4th edition. ISBN 978-0072831153, 432 pp.
Van de Poel, I., and L. Royakkers (2011). Ethics, Technology, and Engineering: An Introduction, Wiley-Blackwell. ISBN 978-1-444-33095-3, 376 pp.
== External links ==
=== Australia ===
Association of Professional Engineers, Scientists and Managers, Australia
Ethical Decision Making
Engineers Australia
Code of Ethics Archived 2012-05-26 at the Wayback Machine

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=== Canada ===
Association of Professional Engineers and Geoscientists of British Columbia (APEGBC)
Act, Bylaws and Code of Ethics
Association of Professional Engineers, and Geoscientists of Alberta (APEGA)
EGGP Code of Ethics
Association of Professional Engineers and Geoscientists of Manitoba (APEGM)
Code of Ethics
Professional Engineers Ontario (PEO)
Code of Ethics (See link on front page.)
L'Ordre des ingénieurs du Québec (OIQ)
Code of Ethics of Engineers Archived 2007-09-19 at the Wayback Machine
Iron Ring
The Ritual of the Calling of an Engineer
University of Western Ontario
Software Ethics - A Guide to the Ethical and Legal Use of Software for Members of the University Community of the University of Western Ontario
=== Germany ===
Verein Deutscher Ingenieure
Ethical principles of engineering profession Archived 2016-03-04 at the Wayback Machine
=== Ireland ===
Engineers Ireland
Code of Ethics Archived 2019-12-13 at the Wayback Machine
=== Sri Lanka ===
Institution of Engineers, Sri Lanka
Code of Ethics Archived 2019-05-12 at the Wayback Machine
=== Turkey ===
Union of Chambers of Turkish Engineers and Architects
Professional Behavior Principles
=== United Kingdom ===
Association for Consultancy and Engineering (ACE)
Anti-Corruption Action Statement
Engineering Professors' Council (EPC)
Engineering Ethics Toolkit
Ethics Explorer
Institution of Civil Engineers (ICE)
Royal Charter, By-laws, Regulations and Rules
Institution of Engineering and Technology (IET)
Professional ethics and the IET
Engineering Council (EC)
Joint Statement of Ethical Principles Archived 2015-02-05 at the Wayback Machine
=== United States ===
National Academy of Engineering
Online Ethics Center of the National Academy of Engineering Archived 2018-02-01 at the Wayback Machine
List of links to various professional and scientific societies' codes of ethics
Onlineethics.org
National Institute for Engineering Ethics (NIEE)
National Society of Professional Engineers (NSPE)
Code of Ethics
Board of Ethical Review and BER Cases
Ethics Resources and References
American Institute of Chemical Engineers (AIChE)
Code of Ethics
American Society of Civil Engineers (ASCE)
Code of Ethics
Standards of Professional Conduct for Civil Engineers
American Society of Mechanical Engineers (ASME), Code of Ethics
Institute of Electrical and Electronics Engineers (IEEE)
Code of Ethics
The Order of the Engineer
The Obligation of an Engineer
Society of Manufacturing Engineers (SME)
Code of Ethics
=== International ===
Global Infrastructure Anti-Corruption Centre
Transparency International

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Equiveillance is a state of equilibrium, or a desire to attain a state of equilibrium, between surveillance and sousveillance. It is sometimes confused with transparency. The balance (equilibrium) provided by equiveillance allows individuals to construct their own cases from evidence they gather themselves, rather than merely having access to surveillance data that could possibly incriminate them.
The Dutch perspective on equiveillance puts it in a sociopolitical context in regards to a balance between individuals and the state.
Equiveillance uses sousveillance, in addition to transparency, to preserve the contextual integrity of surveillance data. For example, lifelong capture of personal experiences provides alternative viewpoints in addition to external surveillance data, to prevent the surveillance-only data from being taken out-of-context.
== Ubiquitous computing ==
Equiveillance represents a situation where all parties of a society or economy are empowered to be able to use the tools of accountability to make beneficial decisions. The increasing trend to record information from our environment, and of ourselves creates the need to delineate the relationships between privacy, surveillance, and sousveillance.
Equiveillance addresses the balance between ubiquitous computing (computing installed throughout our environment) and wearable computing (computing installed upon our own bodies). As personal cell phones store more information and have the capacity to share it, wearable and mobile computing makes manifest the ability for an individual, or small group of individuals, to monitor larger institutional systems with a goal of developing systems of transparency and accountability.
In the same way that large institutions, such as governments or corporations, store information about the buying habits of the public through integrated surveillance practice and ubiquitous computing infrastructure, individuals can act as consumer activists though a system of inverse surveillance that is based upon a wearable computing infrastructure that assists in maximizing personal privacy and alerting one of information being recorded about the self. Such actions lead to an equiveillant state, as power and respect are shared in a more balanced way.
Panoptic surveillance was described by Michel Foucault in the context of a prison in which prisoners were isolated from each other but visible at all times by guards. Surveillance isolates individuals from one another by setting forth a one-way visibility to authority figures, leading to social fragmentation.
Sousveillance has a community-based origin, such as a personal electronic diary (or weblog), made public on the World Wide Web. Sousveillance brings together individuals, by influencing a large city to function with the social connectivity of a small town, with the pitfalls of gossip, but also the benefits of a sense of community participation, where the sousveillance environment generates a greater sense of responsibility.
Ubiquitous computing ("ubicomp"), also known as pervasive computing ("pervcomp"), is the integration of computers with the environment. Ubiquitous computing tends to rely on cooperation of the immediate infrastructure in the environment, but also has a tendency to centralize information, and hence, centralize authority structures. It also creates segregation, and has implications for social rights such as education and healthcare. Individuals are sorted and classified within a ubiquitous computing environment, leading to a new form of segregation. Ubiquitous computing also places emphasis on copyright law and undermines creative environments due to the controlling tendencies of authority.
Wearable computing ("wearcomp") refers to portable, wearable computing technologies. Wearcomp doesn't require any special infrastructure in the environment, as the computer is self-contained and self-reliant. With sousveillant computing, it is possible for the focus of control to be more distributed rather than centralized.
A free society is one which places emphasis on respect and the balance of power: in a democratic society, respect and power are shared and well distributed, whereas in a despotic community, respect and power are not shared and are restricted to the few. Increasingly, our society is confronted with the realization of a ubiquitous computing environment, with the infrastructure predicated upon sensor and surveillance systems to function despite efforts to stop such expansions. How we participate in sharing respect and power will converge with how our society conducts surveillance of its citizens, and how citizens conduct sousveillance. Equiveillance represents a harmonious balance that maximizes human freedom, individual rights as well as communal democracy. The field of personal cybernetics will converge with the fields of personal imaging and glogging (CyborgLogging), as individuals store and archive information for personal use and as a form of self-defense.
== Equiveillance table ==
Equiveillance establishes a social balance between surveillance and sousveillance, as outlined in a general series of comparisons that is known in the published literature as the "equiveillance table". There are two kinds of situations that occur when this social balance does not exist: inequiveillance, in which there is a one-sided nature to surveillance (this is the most common situation), and disequiveillance, which is when the balance is not provably one-sided, but, rather, is unequal but not clearly in one or the other direction.

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== Inequiveillance (disequiveillance) ==
Equiveillance represents a balance of the power relationships that surveillance and sousveillance touch upon. When there is an imbalance, social consequences can range from loss of privacy, to social reactions that in the aggregate lead to unrest and political instability. The idea of disequiveillance is described by Paul Virilio in his treatise on Dromology and the possibility of freedom loss as an accident of our modern world and how it relates to terrorism and war. In this context, the lack of equiveillance (disequiveillance) refers to the anthropological consequences of a world filled with continuous recording devices that encourage a despotic form of government with a tendency to intrude upon the lives of its citizens.
The evolving field of sousveillance, stems in part from recent research on the topic of surveillance and inverse-surveillance, shedding light on how media technology is changing our sense of privacy and human freedom. Privacy becomes increasingly a measure of freedom and its control central to personal autonomy.
Increasingly, remembering is influenced by both personal and public search engines, as computing is becoming increasingly dependent upon the human–computer interaction. The issue of being able to control the amount of personal information that escapes and is recorded in the many machines that make up evolving ubiquitous computing world stresses the importance of equiveillance. The impact of surveillance will be increasingly related to the impact of increasing computer storage space and data mining processing speed.
A 2005 article by Margaret Papandreou entitled "Is nothing Sacred" also highlights the issue of disequiveillance and how the theft of personal communications with her son undermined her freedom of thought. Mrs Papandreou's personal computer was entered by hackers with malicious intent, with multiple emails, and personal information downloaded and eventually published in book form. The issue of freedom of the press, vs theft of personal property and electronic trespassing, developed into a subsequent legal action against the journalist and member of the Greek Parliament, Liana Kanelli. The issue of public vs. private space comes into the debate. The practice of spying as a political technique creates a flashback to how previous Greek politicians where undermined via covert eavesdropping and a subsequent outcome from the previous decades of the Greek Civil War. The complexity of this case becomes twofold when the person surveillanced is a concerned citizen, and not a politician. Furthermore, the issue of taking personal electronic communication out of context for political and financial gain creates the issue of disequiveillance. This can happen on a personal level, or on a larger social, or institutional level.
"The correspondence, which included e-mails, was published in late 2000 and early 2001, in the Nemesis magazine, which was run by journalist and MP Liana Kanelli. It was unclear how Kanelli laid hands on the letters, in which the US-born Papandreou advised the FM on his political career, urging him to make use of non-governmental organizations such as the Andreas Papandreou Foundation and the Andreas Papandreou Institute for Strategic and
Development Studies (ISTAME). Nemesis claimed Papandreou had been trying to influence Greek politics."
The foundations of human freedom also are rooted in the idea of social contract. Biased comments from the conclusion of documents obtained without a search warrant, and against the principles of legal procedure create an unfair forum for judging and condemnation and expose some of the problems of how electronic freedom can be misused towards a systemic persecution and misrepresentation. It also exposes how social instability can rapidly cause a society to evolve into a prison state. Order is maintained in systematic dissolution of freedom towards a government that operates more like a prison rather than a body of persons made up of "free individuals" with an overemphasis of everyone watching everyone, with anyone becoming an informant for whatever side that may be competing for power.
Unbalanced surveillance, and disequiveillance can rapidly devolve society towards the en-masse phenomena such as racism, scapegoating and even mob reactions towards an individual. It is also of importance to realize that justice is not rooted in vengeance, but rather, the law.
The Margaret Papandreou case highlights the issue of victimization through use of the media as a form of propaganda. The social emphasis of a big brother society is rapidly transitioning Balkan nations via the prevalence of media support of such systems with an increasingly legal disregard for individual privacy.
The issue of privacy as part of freedom is in conflict with an absolutely transparent society where all things become recorded and available. The ability to mediate one's visibility increasingly intersects with the concept of wearable computing, as a form of sheltering the individual from a world filled with recorders and sensors. The ability to control one's personal information is increasingly part of how one is to maintain one's personal and free space. Hence protecting one's privacy also intersects with the concept of sousveillance.
== References ==
== External links ==
[3] Surveillance-and-Society: Sousveillance: Inventing and Using Wearable Computing Devices for Data Collection in Surveillance Environments", Volume 1, Issue 3; pp. 331–55. Steve Mann, Jason Nolan and Barry Wellman.
Exploring Equiveillance (Anonequiveillance, University of Ottawa, Faculty of Law)
Despotism: An Epic Classroom Film, Encyclopædia Britannica Internet Film Archives
William J. Mitchell: On Line Lecture on Me++
William J. Mitchell City of Bits: Space, Place and the InfoBahn
Equiveillance Table comparing Surveillance and Sousveillance
Ten Hypothesis of Equiveillance
Continuous Lifelong Capture of Personal Experience with Eyetap (see sections 7 and 8) Archived 2006-02-06 at the Wayback Machine
Surveillance Works Both Ways. Wired.com.
Publisher acquitted over letters
[4]
Athens News Agency on the Theft of Mrs Papandreou's letters
BBC article on Big Brother TV Show in Greece
BBC article on Big Brother TV show in Greece
Constitution of Greece, article 19

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Ethics in mathematics is an emerging field of applied ethics, the inquiry into ethical aspects of the practice and applications of mathematics. It deals with the professional responsibilities of mathematicians whose work influences decisions with major consequences, such as in law, finance, the military, and environmental science. When understood in its socio-economic context, the development of mathematical works can lead to ethical questions ranging from the handling and manipulation of big data to questions of responsible mathematisation and falsification of models, explainable and safe mathematics, as well as many issues related to communication and documentation. The usefulness of a Hippocratic oath for mathematicians is an issue of ongoing debate among scholars. As an emerging field of applied ethics, many of its foundations are still highly debated. The discourse remains in flux. Especially the notion that mathematics can do harm remains controversial.
The ethical questions surrounding the practice of mathematics can be connected to issues of dual-use. An instrumental interpretation of the impact of mathematics makes it difficult to see ethical consequences, yet it might be easier to see how all branches of mathematics serve to structure and conceptualize solutions to real problems. These structures can set up perverse incentives, where targets can be met without improving services, or league table positions are gamed. While the assumptions written into metrics often reflect the worldview of the groups who are responsible for designing them, they are harder for non-experts to challenge, leading to injustices. As mathematicians can enter the workforce of industrialised nations in many places that are no longer limited to teaching and academia, scholars have made the argument that it is necessary to add ethical training into the mathematical curricula at universities.
The philosophical positions on the relationship between mathematics and ethics are varied. Some philosophers (e.g. Plato) see both mathematics and ethics as rational and similar, while others (e.g. Rudolf Carnap) see ethics as irrational and different from mathematics. Possible tensions between applying mathematics in a social context and its ethics can already be observed in Plato's Republic (Book VIII) where the use of mathematics to produce better guardians plays a critical role in its collapse.
== Need for ethics in the mathematics profession ==
Mathematicians in industrial, scientific, military and intelligence roles crucially influence decisions with significant consequences.
=== Issues of accuracy ===
For example, complex calculations were needed for the success of the Manhattan Project, while the overextended use of the Gaussian copula formula to price derivatives before the 2008 financial crisis has been called "the formula that killed Wall Street", and the theory of global warming depends on the reliability of mathematical models of climate.
=== Issues of impact ===
For the same reason as in medical ethics and engineering ethics, the high impact of the consequences of decisions imposes serious ethical obligations on practitioners to consider the rights and wrongs of their advice and decisions. The potential impact of data and new technology is leading more professions, such as accountancy, to consider how bias is overseen in automated systems, from algorithms to AI. Due to its large impact and its necessity in the modern industrialised world, mathematics has been labelled as a new factor of production by some scholars. Mathematics is a fundamental driver of today's economies and plays an everyday role in the decision making in capitalist markets. When studied in its socio-economic context, the debates surrounding the ethical use of mathematics often go under different names, e.g. some people speak of the ethics of quantification. These discourses are often disjoint from those directly affecting or driven by parts of the mathematical community.
== Disasters involving the use of mathematics ==
These illustrate the major consequences of numerical mistakes and hence the need for ethical care.
The Club of Rome's 1972 mathematical-model-based predictions in The Limits to Growth of widespread collapse of the world system by the end of the 21st century.
The wrongful conviction of Sally Clark (1999), An English solicitor, Sally Clark, was wrongfully convicted of murdering her two children – each of whom had died due to sudden infant death syndrome – due to a fundamental statistical error in the testimony of an "expert". The error was further compounded by the "prosecutor's fallacy".
== Ethical issues in the mathematical profession ==
Mathematicians have a professional responsibility to support the ethical use of mathematics in practice, both to sustain the reputation of the profession and to protect society from the impacts of unethical behavior. For example, mathematics is extensively applied in the use of Big Data in Artificial Intelligence applications, both by mathematicians and non-mathematicians, with complex impacts that are not readily understood or anticipated.
== Ethics in data journalism ==
Journalism has established Professional ethics which is affected by mathematical processing and (re-)publication of sources. Reusing information packaged as facts require checking, and validating, from conceptual confusion to sampling and calculation errors. Other professional issues arise from the potential of automated tools which allow the dissemination of publicly available data which has never been collated.
== Misuse of statistics ==
Applications of mathematics generally involve drawing conclusions from quantitative data. Due to uncertainties that mathematical models deal with, and challenges in drawing and communicating any conclusions, there is a possibility of mathematicians misleading the clients as they are not generally aware of quantitative techniques. To avoid such instances, statisticians codified their ethics in the 1980s in a declaration of the ISI, recognizing that there would often be conflicting demands from stakeholders, with ethical decisions a matter of professional judgment.
== Mathematical folklore ==
Priority and attribution of mathematical discovery are important to professional practice, even as some theorems bear the name of the person making the conjecture rather than finding the proof. Folk theorems, or mathematical folklore cannot be attributed to an individual, and may not have an agreed proof, despite being an accepted result, potentially leading to injustice.

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== Ethics in pure mathematical research ==
The American Mathematical Society publishes a code of ethical guidelines for mathematical researchers. The responsibilities of researchers include being knowledgeable in the field, avoiding plagiarism, giving credit, publishing without unreasonable delay, and correcting errors. The European Mathematical Society Ethics Committee also publishes a code of practice relating to the publication, editing and refereeing of research.
It has been argued that as pure mathematical research is relatively harmless, it raises few urgent ethical issues. However, that raises the question of whether and why pure mathematics is ethically worth doing, given that it consumes the lives of many highly intelligent people who could be making more immediately useful contributions.
The study of ethical challenges in pure mathematics is deeply connected to the philosophy of mathematical practice. Arguments against the ethical neutrality of pure mathematical work often builds on the social constitution, i.e. the socio-cultural context of the research and the many decisions involved in mathematical proofs. The problem of epistemic injustice in mathematical research is actively discussed in this context.
== Parallels between ethics and mathematics ==
Ethics and mathematics both appear to rely on reasoning from intuition, unlike empirical sciences which rely fundamentally on observations and experiments. That has been suggested as a reason in support of objectivity or moral realism in ethics, since arguments against objectivity in ethics are paralleled by arguments against objectivity in mathematics, which is generally believed to be false.
Justin Clarke-Doane argues to the contrary that although mathematics and ethics are closely parallel, a pluralist attitude should be taken to the truths of both. Just as the parallel postulate is true in Euclidean geometry but false in non-Euclidean geometry, so ethical propositions can be true or false in different systems.
== Teaching ethics in mathematics ==
Courses in the ethics of mathematics remain rare. The University of New South Wales taught a compulsory course on Professional Issues and Ethics in Mathematics in its mathematics degrees from 1998 to 2012. In 2023, the ETH Zurich taught an optional seminar on ethics in mathematics and a non-examinable seminar also exists at the University of Cambridge. A mini-seminar has also been taught at Swarthmore College.
Many courses considering ethics in mathematics also appear under different names, e.g. "mathematics for social justice."
Similar approaches can also be found in the teaching of ethics to computer science students, where the term "embedded ethics" has established itself for the integration of ethics teaching into the curriculum. These programmes are currently explored at Harvard University, Stanford University and other places.
== See also ==
Big data ethics – Ethics of mass data analytics
Critical mathematics pedagogy – Liberation-focused math education
Essentially contested concept – Problem in philosophy
Ethical calculus – Application of mathematics to calculate issues in ethics
Ethics of artificial intelligence
Ethics of quantification
Misuse of statistics – Use of statistical arguments to assert falsehoods
Prosecutor's fallacy – Logic error due to ignoring the base ratePages displaying short descriptions of redirect targets
Type I and type II errors – Concepts from statistical hypothesis testing
Type III error – Term in statistical hypothesis testing
Unintended consequences – Unforeseen outcomes of an action
== Notes ==
== References ==
== External links ==
Cambridge University Ethics in Mathematics Society
American Mathematical Society: Policy Statement on Ethical Guidelines
Royal Statistical Society: Statistics and the Law.

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The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes algorithmic biases, fairness, accountability, transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks.
Some application areas may also have particularly important ethical implications, like healthcare, education, criminal justice, or the military.
== Machine ethics ==
Machine ethics (or machine morality) is the field of research concerned with designing Artificial Moral Agents (AMAs), robots or artificially intelligent computers that behave morally or as though moral. To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency, which are related to the concept of AMAs.
There are discussions on creating tests to see if an AI is capable of making ethical decisions. Alan Winfield concludes that the Turing test is flawed and the requirement for an AI to pass the test is too low. A proposed alternative test is one called the Ethical Turing Test, which would improve on the current test by having multiple judges decide if the AI's decision is ethical or unethical. Neuromorphic AI could be one way to create morally capable robots, as it aims to process information similarly to humans, nonlinearly and with millions of interconnected artificial neurons. Similarly, whole-brain emulation (scanning a brain and simulating it on digital hardware) could also in principle lead to human-like robots, thus capable of moral actions. And large language models are capable of approximating human moral judgments. Inevitably, this raises the question of the environment in which such robots would learn about the world and whose morality they would inherit – or if they end up developing human 'weaknesses' as well: selfishness, pro-survival attitudes, inconsistency, scale insensitivity, etc.
In Moral Machines: Teaching Robots Right from Wrong, Wendell Wallach and Colin Allen conclude that attempts to teach robots right from wrong will likely advance understanding of human ethics by motivating humans to address gaps in modern normative theory and by providing a platform for experimental investigation. As one example, it has introduced normative ethicists to the controversial issue of which specific learning algorithms to use in machines. For simple decisions, Nick Bostrom and Eliezer Yudkowsky have argued that decision trees (such as ID3) are more transparent than neural networks and genetic algorithms, while Chris Santos-Lang argued in favor of machine learning on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable to criminal "hackers".
Some researchers frame machine ethics as part of the broader AI control or value alignment problem: the difficulty of ensuring that increasingly capable systems pursue objectives that remain compatible with human values and oversight. Stuart Russell has argued that beneficial systems should be designed to (1) aim at realizing human preferences, (2) remain uncertain about what those preferences are, and (3) learn about them from human behaviour and feedback, rather than optimizing a fixed, fully specified goal. Some authors argue that apparent compliance with human values may reflect optimization for evaluation contexts rather than stable internal norms, complicating the assessment of alignment in advanced language models.
== Challenges ==
=== Algorithmic biases ===
AI has become increasingly inherent in facial and voice recognition systems. These systems may be vulnerable to biases and errors introduced by their human creators. Notably, the data used to train them can have biases.
According to Allison Powell, associate professor at LSE and director of the Data and Society programme, data collection is never neutral and always involves storytelling. She argues that the dominant narrative is that governing with technology is inherently better, faster and cheaper, but proposes instead to make data expensive, and to use it both minimally and valuably, with the cost of its creation factored in. Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias. In natural language processing, problems can arise from the text corpus—the source material the algorithm uses to learn about the relationships between different words.
Large companies such as IBM, Google, etc. that provide significant funding for research and development have made efforts to research and address these biases. One potential solution is to create documentation for the data used to train AI systems. Process mining can be an important tool for organizations to achieve compliance with proposed AI regulations by identifying errors, monitoring processes, identifying potential root causes for improper execution, and other functions. However, there are also limitations to the current landscape of fairness in AI, due to the intrinsic ambiguities in the concept of discrimination, both at the philosophical and legal level.

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==== Racial and gender biases ====
Bias can be introduced through historical data used to train AI systems. For instance, Amazon terminated their use of AI hiring and recruitment because the algorithm favored male candidates over female ones. This was because Amazon's system was trained with data collected over a 10-year period that included mostly male candidates. The algorithms learned the biased pattern from the historical data, and generated predictions where these types of candidates were most likely to succeed in getting the job. Therefore, the recruitment decisions made by the AI system turned out to be biased against female and minority candidates.
The performance of facial recognition and computer vision models may vary based on race and gender. Facial recognition algorithms made by Microsoft, IBM and Face++ all performed significantly worse on darker-skinned women. Facial recognition was shown to be biased against those with darker skin tones. AI systems may be less accurate for black people, as was the case in the development of an AI-based pulse oximeter that overestimated blood oxygen levels in patients with darker skin, causing issues with their hypoxia treatment. In 2015, controversy erupted after a Black couple were labeled "Gorillas" by Google Photos. Oftentimes the systems are able to easily detect the faces of white people while being unable to register the faces of people who are black. This has led to the ban of police usage of AI materials or software in some U.S. states. The reason for these biases is that AI pulls information from across the internet to influence its responses in each situation. For example, if a facial recognition system was only tested on people who were white, it would make it much harder for it to interpret the facial structure and tones of other races and ethnicities. Biases often stem from the training data rather than the algorithm itself, notably when the data represents past human decisions.
A 2020 study that reviewed voice recognition systems from Amazon, Apple, Google, IBM, and Microsoft found that they have higher error rates when transcribing black people's voices than white people's.
Injustice in the use of AI is much harder to eliminate within healthcare systems, as oftentimes diseases and conditions can affect different races and genders differently. This can lead to confusion as the AI may be making decisions based on statistics showing that one patient is more likely to have problems due to their gender or race. This can be perceived as a bias because each patient is a different case, and AI is making decisions based on what it is programmed to group that individual into. This leads to a discussion about what should be considered a biased decision in the distribution of treatment. While it is known that there are differences in how diseases and injuries affect different genders and races, there is a discussion on whether it is fairer to incorporate this into healthcare treatments, or to examine each patient without this knowledge. In modern society there are certain tests for diseases, such as breast cancer, that are recommended to certain groups of people over others because they are more likely to contract the disease in question. If AI implements these statistics and applies them to each patient, it could be considered biased.
In the justice system, AI can have biases against black people, labeling black court participants as high-risk at a much larger rate than white participants. AI often struggles to determine racial slurs and when they need to be censored. It struggles to determine when certain words are being used as a slur and when it is being used culturally. The COMPAS program has been used to predict which defendants are more likely to reoffend. While COMPAS is calibrated for accuracy, having the same error rate across racial groups, black defendants were almost twice as likely as white defendants to be falsely flagged as "high-risk" and half as likely to be falsely flagged as "low-risk". Another example is within Google's ads that targeted men with higher-paying jobs and women with lower-paying jobs. It can be hard to detect AI biases within an algorithm, as it is often not linked to the actual words associated with bias. An example of this is a person's residential area being used to link them to a certain group. This can lead to problems, as oftentimes businesses can avoid legal action through this loophole. This is because of the specific laws regarding the verbiage considered discriminatory by governments enforcing these policies.
Large language models often reinforce gender stereotypes, assigning roles and characteristics based on traditional gender norms. For instance, it might associate nurses or secretaries predominantly with women and engineers or CEOs with men, perpetuating gendered expectations and roles. Additionally, facial recognition, computer vision, or automatic gender recognition models can reinforce bias against both cisgender and transgender people through misclassification of gender that is misaligned with the person's identity.

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== History ==
Historically speaking, the investigation of moral and ethical implications of "thinking machines" goes back at least to the Enlightenment: Leibniz already posed the question of whether we should attribute intelligence to a mechanism that behaves as if it were a sentient being, and so does Descartes, who describes what could be considered an early version of the Turing test.
The romantic period has several times envisioned artificial creatures that escape the control of their creator with dire consequences, most famously in Mary Shelley's Frankenstein. The widespread preoccupation with industrialization and mechanization in the 19th and early 20th century, however, brought ethical implications of unhinged technical developments to the forefront of fiction: R.U.R – Rossum's Universal Robots, Karel Čapek's play of sentient robots endowed with emotions used as slave labor is not only credited with the invention of the term 'robot' (derived from the Czech word for forced labor, robota) but was also an international success after it premiered in 1921. George Bernard Shaw's play Back to Methuselah, published in 1921, questions at one point the validity of thinking machines that act like humans; Fritz Lang's 1927 film Metropolis shows an android leading the uprising of the exploited masses against the oppressive regime of a technocratic society.
In the 1950s, Isaac Asimov considered the issue of how to control machines in I, Robot. At the insistence of his editor John W. Campbell Jr., he proposed the Three Laws of Robotics to govern artificially intelligent systems. Much of his work was then spent testing the boundaries of his three laws to see where they would break down, or where they would create paradoxical or unanticipated behavior. His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances. More recently, academics and many governments have challenged the idea that AI can itself be held accountable. A panel convened by the United Kingdom in 2010 revised Asimov's laws to clarify that AI is the responsibility either of its manufacturers, or of its owner/operator.
Eliezer Yudkowsky, from the Machine Intelligence Research Institute, suggested in 2004 a need to study how to build a "Friendly AI", meaning that there should also be efforts to make AI intrinsically friendly and humane.
In 2009, academics and technical experts attended a conference organized by the Association for the Advancement of Artificial Intelligence to discuss the potential impact of robots and computers, and the impact of the hypothetical possibility that they could become self-sufficient and make their own decisions. They discussed the possibility and the extent to which computers and robots might be able to acquire any level of autonomy, and to what degree they could use such abilities to possibly pose any threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able to independently choose targets to attack with weapons. They also noted that some computer viruses can evade elimination and have achieved "cockroach intelligence". They noted that self-awareness as depicted in science-fiction is probably unlikely, but that there were other potential hazards and pitfalls.
Also in 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne, Switzerland, robots that were programmed to cooperate with each other (in searching out a beneficial resource and avoiding a poisonous one) eventually learned to lie to each other in an attempt to hoard the beneficial resource.
== Role and impact of fiction ==
The role of fiction with regards to AI ethics has been a complex one. One can distinguish three levels at which fiction has impacted the development of artificial intelligence and robotics: Historically, fiction has prefigured common tropes that have not only influenced goals and visions for AI, but also outlined ethical questions and common fears associated with it. During the second half of the twentieth and the first decades of the twenty-first century, popular culture, in particular movies, TV series and video games have frequently echoed preoccupations and dystopian projections around ethical questions concerning AI and robotics. Recently, these themes have also been increasingly treated in literature beyond the realm of science fiction. And, as Carme Torras, research professor at the Institut de Robòtica i Informàtica Industrial (Institute of robotics and industrial computing) at the Technical University of Catalonia notes, in higher education, science fiction is also increasingly used for teaching technology-related ethical issues in technological degrees.
=== TV series ===
While ethical questions linked to AI have been featured in science fiction literature and feature films for decades, the emergence of the TV series as a genre allowing for longer and more complex story lines and character development has led to some significant contributions that deal with ethical implications of technology. The Swedish series Real Humans (2012–2013) tackled the complex ethical and social consequences linked to the integration of artificial sentient beings in society. The British dystopian science fiction anthology series Black Mirror (2013–Present) is particularly notable for experimenting with dystopian fictional developments linked to a wide variety of recent technology developments. Both the French series Osmosis (2020) and British series The One deal with the question of what can happen if technology tries to find the ideal partner for a person. Several episodes of the Netflix series Love, Death+Robots have imagined scenes of robots and humans living together. The most representative one of them is S02 E01, which shows how bad the consequences can be when robots get out of control if humans rely too much on them in their lives.

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=== Future visions in fiction and games ===
The movie The Thirteenth Floor suggests a future where simulated worlds with sentient inhabitants are created by computer game consoles for the purpose of entertainment. The movie The Matrix suggests a future where the dominant species on planet Earth are sentient machines and humanity is treated with utmost speciesism. The short story "The Planck Dive" suggests a future where humanity has turned itself into software that can be duplicated and optimized and the relevant distinction between types of software is sentient and non-sentient. The same idea can be found in the Emergency Medical Hologram of Starship Voyager, which is an apparently sentient copy of a reduced subset of the consciousness of its creator, Dr. Zimmerman, who, for the best motives, has created the system to give medical assistance in case of emergencies. The movies Bicentennial Man and A.I. deal with the possibility of sentient robots that could love. I, Robot explored some aspects of Asimov's three laws. All these scenarios try to foresee possibly unethical consequences of the creation of sentient computers.
Over time, debates have tended to focus less and less on possibility and more on desirability, as emphasized in the "Cosmist" and "Terran" debates initiated by Hugo de Garis and Kevin Warwick.
== See also ==
== References ==
== External links ==
Ethics of Artificial Intelligence at the Internet Encyclopedia of Philosophy
Ethics of Artificial Intelligence and Robotics at the Stanford Encyclopedia of Philosophy
The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence
Russell S, Hauert S, Altman R, Veloso M (May 2015). "Robotics: Ethics of artificial intelligence". Nature. 521 (7553): 415–418. Bibcode:2015Natur.521..415.. doi:10.1038/521415a. PMID 26017428. S2CID 4452826.
AI Ethics Guidelines Global Inventory by Algorithmwatch
Hagendorff T (March 2020). "The Ethics of AI Ethics: An Evaluation of Guidelines". Minds and Machines. 30 (1): 99–120. arXiv:1903.03425. doi:10.1007/s11023-020-09517-8. S2CID 72940833.
Sheludko, M. (December, 2023). Ethical Aspects of Artificial Intelligence: Challenges and Imperatives. Software Development Blog.
Eisikovits N. "AI Is an Existential Threat—Just Not the Way You Think". Scientific American. Retrieved 2024-03-04.
Anwar U, Saparov A, Rando J, Paleka D, Turpin M, Hase P, Lubana ES, Jenner E, Casper S, Sourbut O, Edelman BL, Zhang Z, Günther M, Korinek A, Hernandez-Orallo J, Hammond L, Bigelow E, Pan A, Langosco L, Krueger D (2024). "Foundational Challenges in Assuring Alignment and Safety of Large Language Models". arXiv:2404.09932 [cs.LG].

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==== Stereotyping ====
Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways. For instance, scholars highlighted how AI systems can reproduce and amplify global inequalities, particularly when data and model development are concentrated in Western countries, raising concerns about fairness and representation in AI systems.
Such stereotypes stem directly from the design of AI systems and programmatic models from which they are trained. Stereotypes that target specific demographics originate from societal biases embedded during the programming process, outdated datasets, and algorithmic architectures that prioritize high-ranking and majority groups rather than underrepresented ones. Research also amplifies user feedback as a primary contributor to stereotypes within AI, as human interactions introduce bias. Additionally, the AI industry is a male-dominant field, primarily young adult males, creating a lack of diversity that cultivates inequalities in AI databases. Word embeddings reveal that the use of "person/people" within AI algorithms displays gender inequality, as it prioritizes men over women rather than neutrality.
==== Language bias ====
AI is primarily trained on English. Celeste Rodriguez Louro has argued that mainstream American English is the primary variety of English used to train generative AI systems, resulting in a linguistic bias toward homogeneity and the exclusion of other varieties of English. Since current large language models are predominantly trained on English-language data, they often present Western views as truth, while systematically downplaying non-English perspectives. As of 2024, most AI systems are trained on only 100 of the 7,000 world languages.
==== Political bias ====
Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data. This skewing of the data is known as algorithmic bias, or when an AI has a predisposition to certain answers based on the data that the AI was trained on. This can create an AI system that is not giving objective answers, but rather skewed answers that lean towards differing ends of the political spectrum. It is said that ChatGPT is a more liberal skewed AI model. It has been found that users are more likely to agree with answers that coincide with their existing political beliefs. Some AI systems try to gauge the political affiliation of the user so that the generated answers can be politically skewed to align with the user, leading to a never-ending confirmation bias loop. It is more difficult for users to perceive a political bias if they already align with the answer, allowing these AI companies and programmers to ultimately get away with their politically biased AI models.
=== Dominance by tech giants ===
The commercial AI scene is dominated by Big Tech companies, including Alphabet Inc., Amazon, Apple Inc., Meta Platforms, Microsoft, and SpaceX. Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace. Their current dominance within the market of technology makes it very hard for newer companies to compete and be successful in the long-run within the industry. It has been suggested by competition law scholars that the tech giants of the world may be using their power within the market to foreclose the market from potential competitors and, in turn, charge higher prices to consumers. In light of some of these concerns, governments around the world have been considering and implementing laws that would prevent large companies from continuing or executing these practices. These tech giants have the money that it takes to build the infrastructure needed nowadays. The five biggest are projected to spend $602 billion in 2026 on capital expenditures alone, which would be a 32% increase from the year prior. In this spending, it is estimated that 75% will go towards AI-specific infrastructure. With the significant growth that has been seen in the tech industry with AI, it is important to keep the industry competitive and fair.
=== Climate impacts ===
The largest generative AI models require significant computing resources to train and use. These computing resources are often concentrated in massive data centers. The resulting environmental impacts include greenhouse gas emissions, water consumption, and electronic waste. Despite improved energy efficiency, the energy needs are expected to increase, as AI gets more broadly used.
==== Electricity consumption and carbon footprint ====
These resources are often concentrated in massive data centers, which require demanding amounts of energy, resulting in increased greenhouse gas emissions. A 2023 study suggests that the amount of energy required to train large AI models was equivalent to 626,000 pounds of carbon dioxide or the same as 300 round-trip flights between New York and San Francisco.

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==== Water consumption ====
In addition to carbon emissions, these data centers also need water for cooling AI chips. Locally, this can lead to water scarcity and the disruption of ecosystems. Around two liters of water are needed per each kilowatt hour of energy used in a data center. While data centers use water for cooling AI chips, there are also many indirect uses that negatively impact the environment. Over 80% of total water consumption comes from electricity generation that is used to fuel these large-scale data centers. In addition to this, around 2/3 of data centers built are placed in water-scarce regions. Because of this, AI development can compete with local communities and agriculture for water usage. A lot of companies do not fully disclose the severity of their impact on water consumption, which raises ethical concerns on whether these companies are truly for the people or if they are looking for maximum profit. A solution these data centers have implemented is to use zero-water air-cooling systems, but this results in higher carbon emissions and increased electricity usage. Companies have to decide to prioritize the local concern of water usage or the global concern of carbon emissions. With only a single AI query, 16.9mL of water is used, but only 2.2mL goes towards the cooling of the systems. This is less than 15% of the total water used in the interaction, which exemplifies the severity of indirect water usage.
==== Electronic waste ====
Another problem is the resulting electronic waste (or e-waste). This can include hazardous materials and chemicals, such as lead and mercury, resulting in the contamination of soil and water. In order to prevent the environmental effects of AI-related e-waste, better disposal practices and stricter laws may be put in place.
==== Prospective ====
The rising popularity of AI increases the need for data centers and intensifies these problems. There is also a lack of transparency from AI companies about the environmental impacts. Some applications can also indirectly affect the environment. For example, AI advertising can increase consumption of fast fashion, an industry that already produces significant emissions.
However, AI can also be used in a positive way by helping to mitigate the environmental damages. Different AI technologies can help monitor emissions and develop algorithms to help companies lower their emissions.
=== Open source ===
Bill Hibbard argues that because AI will have such a profound effect on humanity, AI developers are representatives of future humanity and thus have an ethical obligation to be transparent in their efforts. Organizations like Hugging Face and EleutherAI have been actively open-sourcing AI software. Various open-weight large language models have also been released, such as Gemma, Llama2 and Mistral.
However, making code open source does not make it comprehensible, which by many definitions means that the AI code is not transparent. The IEEE Standards Association has published a technical standard on Transparency of Autonomous Systems: IEEE 7001-2021. The IEEE effort identifies multiple scales of transparency for different stakeholders.
There are also concerns that releasing AI models may lead to misuse. For example, Microsoft has expressed concern about allowing universal access to its face recognition software, even for those who can pay for it. Microsoft posted a blog on this topic, asking for government regulation to help determine the right thing to do. Furthermore, open-weight AI models can be fine-tuned to remove any countermeasure, until the AI model complies with dangerous requests, without any filtering. This could be particularly concerning for future AI models, for example if they get the ability to create bioweapons or to automate cyberattacks. OpenAI, initially committed to an open-source approach to the development of artificial general intelligence (AGI), eventually switched to a closed-source approach, citing competitiveness and safety reasons. Ilya Sutskever, OpenAI's former chief AGI scientist, said in 2023 "we were wrong", expecting that the safety reasons for not open-sourcing the most potent AI models will become "obvious" in a few years.
=== Strain on open knowledge platforms ===
In April 2023, Wired reported that Stack Overflow, a popular programming help forum with over 50 million questions and answers, planned to begin charging large AI developers for access to its content. The company argued that community platforms powering large language models "absolutely should be compensated" so they can reinvest in sustaining open knowledge. Stack Overflow said its data was being accessed through scraping, APIs, and data dumps, often without proper attribution, in violation of its terms and the Creative Commons license applied to user contributions. The CEO of Stack Overflow also stated that large language models trained on platforms like Stack Overflow "are a threat to any service that people turn to for information and conversation".
Aggressive AI crawlers have increasingly overloaded open-source infrastructure, "causing what amounts to persistent distributed denial-of-service (DDoS) attacks on vital public resources", according to a March 2025 Ars Technica article. Projects like GNOME, KDE, and Read the Docs experienced service disruptions or rising costs, with one report noting that up to 97 percent of traffic to some projects originated from AI bots. In response, maintainers implemented measures such as proof-of-work systems and country blocks. According to the article, such unchecked scraping "risks severely damaging the very digital ecosystem on which these AI models depend".
In April 2025, the Wikimedia Foundation reported that automated scraping by AI bots was placing strain on its infrastructure. Since early 2024, bandwidth usage had increased by 50 percent due to large-scale downloading of multimedia content by bots collecting training data for AI models. These bots often accessed obscure and less-frequently cached pages, bypassing caching systems and imposing high costs on core data centers. According to Wikimedia, bots made up 35 percent of total page views but accounted for 65 percent of the most expensive requests. The Foundation noted that "our content is free, our infrastructure is not" and warned that "this creates a technical imbalance that threatens the sustainability of community-run platforms".

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=== Transparency ===
Approaches like machine learning with neural networks can result in computers making decisions that neither they nor their developers can explain. It is difficult for people to determine if such decisions are fair and trustworthy, leading potentially to bias in AI systems going undetected, or people rejecting the use of such systems. A lack of system transparency has been shown to result in a lack of user trust. Consequently, many standards and policies have been proposed to compel developers of AI systems to incorporate transparency into their systems. This push for transparency has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to providing reasons for the model's outputs, and interpretability focusing on understanding the inner workings of an AI model.
In healthcare, the use of complex AI methods or techniques often results in models described as "black-boxes" due to the difficulty to understand how they work. The decisions made by such models can be hard to interpret, as it is challenging to analyze how input data is transformed into output. This lack of transparency is a significant concern in fields like healthcare, where understanding the rationale behind decisions can be crucial for trust, ethical considerations, and compliance with regulatory standards. Trust in healthcare AI has been shown to vary depending on the level of transparency provided. Moreover, unexplainable outputs of AI systems make it much more difficult to identify and detect medical error.
=== Accountability ===
A special case of the opaqueness of AI is that caused by it being anthropomorphised, that is, assumed to have human-like characteristics, resulting in misplaced conceptions of its moral agency. This can cause people to overlook whether either human negligence or deliberate criminal action has led to unethical outcomes produced through an AI system. Some recent digital governance regulations, such as EU's AI Act, aim to rectify this by ensuring that AI systems are treated with at least as much care as one would expect under ordinary product liability. This includes potentially AI audits.
=== Regulation ===
According to a 2019 report from the Center for the Governance of AI at the University of Oxford, 82% of Americans believe that robots and AI should be carefully managed. Concerns cited ranged from how AI is used in surveillance and in spreading fake content online (known as deep fakes when they include doctored video images and audio generated with help from AI) to cyberattacks, infringements on data privacy, hiring bias, autonomous vehicles, and drones that do not require a human controller. Similarly, according to a five-country study by KPMG and the University of Queensland Australia in 2021, 66–79% of citizens in each country believe that the impact of AI on society is uncertain and unpredictable; 96% of those surveyed expect AI governance challenges to be managed carefully.
Not only companies, but many other researchers and citizen advocates recommend government regulation as a means of ensuring transparency, and through it, human accountability. This strategy has proven controversial, as some worry that it will slow the rate of innovation. Others argue that regulation leads to systemic stability more able to support innovation in the long term. The OECD, UN, EU, and many countries are presently working on strategies for regulating AI, and finding appropriate legal frameworks.
On June 26, 2019, the European Commission High-Level Expert Group on Artificial Intelligence (AI HLEG) published its "Policy and investment recommendations for trustworthy Artificial Intelligence". This is the AI HLEG's second deliverable, after the April 2019 publication of the "Ethics Guidelines for Trustworthy AI". The June AI HLEG recommendations cover four principal subjects: humans and society at large, research and academia, the private sector, and the public sector. The European Commission claims that "HLEG's recommendations reflect an appreciation of both the opportunities for AI technologies to drive economic growth, prosperity and innovation, as well as the potential risks involved" and states that the EU aims to lead on the framing of policies governing AI internationally. To prevent harm, in addition to regulation, AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and take accountability to mitigate the risks.
In June 2024, the EU adopted the Artificial Intelligence Act (AI Act). On August 1st 2024, The AI Act entered into force. The rules gradually apply, with the act becoming fully applicable 24 months after entry into force. The AI Act sets rules on providers and users of AI systems. It follows a risk-based approach, where depending on the risk level, AI systems are prohibited or specific requirements need to be met for placing those AI systems on the market and for using them.
=== Deepfakes ===

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Deepfakes are digital media, typically in the form of videos, audio, or images, in which a person’s likeness or voice is digitally replaced or altered using AI. The term “deepfake” is a portmanteau involving the words “deep” and “fake”. Deep is in reference to deep learning such as generative adversarial networks (GANs). The term first emerged and gained widespread attention on the platform Reddit in 2017 after users began sharing non-consensually generated images with each other (MIT Sloan). By the early 2020s, as AI generating software became readily available to many, deepfake became a household term. This term first gained traction on Reddit, where users began sharing non-consensual images among each other. With the advancement of deepfake technology and AI, in specific, we began to see deepfakes of public figures emerge, often involving images of inappropriate or controversial actions. People in the spotlight, especially politicians, are increasingly finding themselves being framed in deepfake videos or images displaying inappropriate symbols or actions (The Guardian). Deepfake technology not only is being used to slander people it is also being used for online scams. In 2022 and 2023, scammers used deepfake technology to mimic the voice of executives, family members, close relatives. The scams resulted in defrauding both businesses and consumers for millions of dollars (CNN).
=== Increasing use ===
AI has been slowly making its presence more known throughout the world, from chatbots that seemingly have answers for every homework question to generative AI that can create a painting about whatever one desires. AI has become increasingly popular in hiring markets, from the ads that target certain people according to what they are looking for to the inspection of applications of potential hires. Events such as COVID-19 have sped up the adoption of AI programs in the application process, due to more people having to apply electronically, and with this increase in online applicants the use of AI made the process of narrowing down potential employees easier and more efficient. AI has become more prominent as businesses have to keep up with the times and ever-expanding internet. Processing analytics and making decisions becomes much easier with the help of AI. As Tensor Processing Units (TPUs) and graphics processing units (GPUs) become more powerful, AI capabilities also increase, forcing companies to use it to keep up with the competition. Managing customers' needs and automating many parts of the workplace leads to companies having to spend less money on employees.
AI has also seen increased usage in criminal justice and healthcare. For medicinal means, AI is being used more often to analyze patient data to make predictions about future patients' conditions and possible treatments. These programs are called clinical decision support systems (DSS). AI's future in healthcare may develop into something further than just recommended treatments, such as referring certain patients over others, leading to the possibility of inequalities.
=== AI welfare ===
In 2020, professor Shimon Edelman noted that only a small portion of work in the rapidly growing field of AI ethics addressed the possibility of AIs experiencing suffering. This was despite credible theories having outlined possible ways by which AI systems may become conscious, such as the global workspace theory or the integrated information theory. Edelman notes one exception had been Thomas Metzinger, who in 2018 called for a global moratorium on further work that risked creating conscious AIs. The moratorium was to run to 2050 and could be either extended or repealed early, depending on progress in better understanding the risks and how to mitigate them. Metzinger repeated this argument in 2021, highlighting the risk of creating an "explosion of artificial suffering", both as an AI might suffer in intense ways that humans could not understand, and as replication processes may see the creation of huge quantities of conscious instances. Podcast host Dwarkesh Patel said he cared about making sure no "digital equivalent of factory farming" happens. In the ethics of uncertain sentience, the precautionary principle is often invoked.
Several labs have openly stated they are trying to create conscious AIs. There have been reports from those with close access to AIs not openly intended to be self aware, that consciousness may already have unintentionally emerged. These include OpenAI founder Ilya Sutskever in February 2022, when he wrote that today's large neural nets may be "slightly conscious". In November 2022, David Chalmers argued that it was unlikely current large language models like GPT-3 had experienced consciousness, but also that he considered there to be a serious possibility that large language models may become conscious in the future. Anthropic hired its first AI welfare researcher in 2024, and in 2025 started a "model welfare" research program that explores topics such as how to assess whether a model deserves moral consideration, potential "signs of distress", and "low-cost" interventions.
According to Carl Shulman and Nick Bostrom, it may be possible to create machines that would be "superhumanly efficient at deriving well-being from resources", called "super-beneficiaries". One reason for this is that digital hardware could enable much faster information processing than biological brains, leading to a faster rate of subjective experience. These machines could also be engineered to feel intense and positive subjective experience, unaffected by the hedonic treadmill. Shulman and Bostrom caution that failing to appropriately consider the moral claims of digital minds could lead to a moral catastrophe, while uncritically prioritizing them over human interests could be detrimental to humanity.
=== Threat to human dignity ===
Joseph Weizenbaum argued in 1976 that AI technology should not be used to replace people in positions that require respect and care, such as:

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A customer service representative (AI technology is already used today for telephone-based interactive voice response systems)
A nursemaid for the elderly (as was reported by Pamela McCorduck in her book The Fifth Generation)
A soldier
A judge
A police officer
A therapist (as was proposed by Kenneth Colby in the 1970s)
Weizenbaum says that humans require authentic feelings of empathy from people in these positions. If machines replace humans, we will find ourselves alienated, devalued and frustrated, for the AI system would not be able to simulate empathy. Artificial intelligence, if used in this way, represents a threat to human dignity. Weizenbaum argues that the fact that we are entertaining the possibility of machines in these positions suggests that we have experienced an "atrophy of the human spirit that comes from thinking of ourselves as computers."
Pamela McCorduck counters that, speaking for women and minorities "I'd rather take my chances with an impartial computer", arguing that there are conditions where it would preferable to have automated judges and police that have no personal agenda at all. However, Kaplan and Haenlein stressed in 2019 that such AI systems are only as smart as the data used to train them since they are, in their essence, nothing more than fancy curve-fitting machines; using AI to support a court ruling can be highly problematic if past rulings show bias toward certain groups since those biases get formalized and ingrained, which makes them even more difficult to spot and fight against.
Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum, these points suggest that AI research devalues human life.
AI founder John McCarthy objects to the moralizing tone of Weizenbaum's critique. "When moralizing is both vehement and vague, it invites authoritarian abuse", he writes. Bill Hibbard writes that "Human dignity requires that we strive to remove our ignorance of the nature of existence, and AI is necessary for that striving."
=== Liability for self-driving cars ===
As the widespread use of autonomous cars becomes increasingly imminent, new challenges raised by fully autonomous vehicles must be addressed. There have been debates about the legal liability of the responsible party if these cars get into accidents. In one report where a driverless car hit a pedestrian, the driver was inside the car but the controls were fully in the hand of computers. This led to a dilemma over who was at fault for the accident.
In another incident on March 18, 2018, Elaine Herzberg was struck and killed by a self-driving Uber in Arizona. In this case, the automated car was capable of detecting cars and certain obstacles in order to autonomously navigate the roadway, but it could not anticipate a pedestrian in the middle of the road. This raised the question of whether the driver, pedestrian, the car company, or the government should be held responsible for her death.
Currently, self-driving cars are considered semi-autonomous, requiring the driver to pay attention and be prepared to take control if necessary. Thus, it falls on governments to regulate drivers who over-rely on autonomous features and to inform them that these are just technologies that, while convenient, are not a complete substitute. Before autonomous cars become widely used, these issues need to be tackled through new policies.
Experts contend that autonomous vehicles ought to be able to distinguish between rightful and harmful decisions since they have the potential of inflicting harm. The two main approaches proposed to enable smart machines to render moral decisions are the bottom-up approach, which suggests that machines should learn ethical decisions by observing human behavior without the need for formal rules or moral philosophies, and the top-down approach, which involves programming specific ethical principles into the machine's guidance system. However, there are significant challenges facing both strategies: the top-down technique is criticized for its difficulty in preserving certain moral convictions, while the bottom-up strategy is questioned for potentially unethical learning from human activities.
=== Weaponization ===

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Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions. The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions. The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue. They point to programs like the Language Acquisition Device which can emulate human interaction.
On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report recommending principles for the ethical use of AI by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. However, a major concern is how the report will be implemented. The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions. Some researchers state that autonomous robots might be more humane, as they could make decisions more effectively. In 2024, the Defense Advanced Research Projects Agency funded a program, Autonomy Standards and Ideals with Military Operational Values (ASIMOV), to develop metrics for evaluating the ethical implications of autonomous weapon systems by testing communities.
Research has studied how to make autonomous systems with the ability to learn using assigned moral responsibilities. "The results may be used when designing future military robots, to control unwanted tendencies to assign responsibility to the robots." From a consequentialist view, there is a chance that robots will develop the ability to make their own logical decisions on whom to kill and that is why there should be a set moral framework that the AI cannot override.
There has been a recent outcry with regard to the engineering of artificial intelligence weapons that have included ideas of a robot takeover of mankind. AI weapons do present a type of danger different from that of human-controlled weapons. Many governments have begun to fund programs to develop AI weaponry. The United States Navy recently announced plans to develop autonomous drone weapons, paralleling similar announcements by Russia and South Korea respectively. Due to the potential of AI weapons becoming more dangerous than human-operated weapons, Stephen Hawking and Max Tegmark signed a "Future of Life" petition to ban AI weapons. The message posted by Hawking and Tegmark states that AI weapons pose an immediate danger and that action is required to avoid catastrophic disasters in the near future.
"If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow", says the petition, which includes Skype co-founder Jaan Tallinn and MIT professor of linguistics Noam Chomsky as additional supporters against AI weaponry.
Physicist and Astronomer Royal Sir Martin Rees has warned of catastrophic instances like "dumb robots going rogue or a network that develops a mind of its own." Huw Price, a colleague of Rees at Cambridge, has voiced a similar warning that humans might not survive when intelligence "escapes the constraints of biology". These two professors created the Centre for the Study of Existential Risk at Cambridge University in the hope of avoiding this threat to human existence.
Regarding the potential for smarter-than-human systems to be employed militarily, the Open Philanthropy Project writes that these scenarios "seem potentially as important as the risks related to loss of control", but research investigating AI's long-run social impact have spent relatively little time on this concern: "this class of scenarios has not been a major focus for the organizations that have been most active in this space, such as the Machine Intelligence Research Institute (MIRI) and the Future of Humanity Institute (FHI), and there seems to have been less analysis and debate regarding them".
Academic Gao Qiqi writes that military use of AI risks escalating military competition between countries and that the impact of AI in military matters will not be limited to one country but will have spillover effects. Gao cites the example of U.S. military use of AI, which he contends has been used as a scapegoat to evade accountability for decision-making.
Under the framework of the Convention on Certain Conventional Weapons, states have discussed lethal autonomous weapon systems since 2014. In 2016, the treaty's states parties established an open-ended Group of Governmental Experts on Lethal Autonomous Weapons Systems to continue those discussions. The discussions have addressed international humanitarian law, accountability, possible prohibitions and regulations, and the extent of human control required over AI-enabled weapons.
A summit was held in 2023 in the Hague on the issue of using AI responsibly in the military domain.
=== Singularity ===

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Vernor Vinge, among numerous others, has suggested that a moment may come when some or all computers will be smarter than humans. The onset of this event is commonly referred to as "the Singularity" and is the central point of discussion in the philosophy of Singularitarianism. While opinions vary as to the ultimate fate of humanity in wake of the Singularity, efforts to mitigate the potential existential risks brought about by AI has become a significant topic of interest in recent years among computer scientists, philosophers, and the public at large.
Many researchers have argued that, through an intelligence explosion, a self-improving AI could become so powerful that humans would not be able to stop it from achieving its goals. In his paper "Ethical Issues in Advanced Artificial Intelligence" and subsequent book Superintelligence: Paths, Dangers, Strategies, philosopher Nick Bostrom argues that AI has the capability to bring about human extinction. He claims that an artificial superintelligence would be capable of independent initiative and of making its own plans, and may therefore be more appropriately thought of as an autonomous agent. Since artificial intellects need not share our human motivational tendencies, it would be up to the designers of the superintelligence to specify its original motivations. Because a superintelligent AI would be able to bring about almost any possible outcome and to thwart any attempt to prevent the implementation of its goals, many uncontrolled unintended consequences could arise. It could kill off all other agents, persuade them to change their behavior, or block their attempts at interference.
However, Bostrom contended that superintelligence also has the potential to solve many difficult problems such as disease, poverty, and environmental destruction, and could help humans enhance themselves.
Unless moral philosophy provides us with a flawless ethical theory, an AI's utility function could allow for many potentially harmful scenarios that conform with a given ethical framework but not "common sense". According to Eliezer Yudkowsky, there is little reason to suppose that an artificially designed mind would have such an adaptation. AI researchers such as Stuart J. Russell, Bill Hibbard, Roman Yampolskiy, Shannon Vallor, Steven Umbrello and Luciano Floridi have proposed design strategies for developing beneficial machines.
== Solutions and approaches ==
To address ethical challenges in artificial intelligence, developers have introduced various systems designed to ensure responsible AI behavior. Examples include Nvidia's Llama Guard, which focuses on improving the safety and alignment of large AI models, and Preamble's customizable guardrail platform. These systems aim to address issues such as algorithmic bias, misuse, and vulnerabilities, including prompt injection attacks, by embedding ethical guidelines into the functionality of AI models.
Prompt injection, a technique by which malicious inputs can cause AI systems to produce unintended or harmful outputs, has been a focus of these developments. Some approaches use customizable policies and rules to analyze inputs and outputs, ensuring that potentially problematic interactions are filtered or mitigated. Other tools focus on applying structured constraints to inputs, restricting outputs to predefined parameters, or leveraging real-time monitoring mechanisms to identify and address vulnerabilities. These efforts reflect a broader trend in ensuring that artificial intelligence systems are designed with safety and ethical considerations at the forefront, particularly as their use becomes increasingly widespread in critical applications.
== Institutions in AI policy and ethics ==
There are many organizations concerned with AI ethics and policy, public and governmental as well as corporate and societal.
Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit, The Partnership on AI to Benefit People and Society, to formulate best practices on artificial intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence. Apple joined in January 2017. The corporate members will make financial and research contributions to the group, while engaging with the scientific community to bring academics onto the board.
The IEEE put together a Global Initiative on Ethics of Autonomous and Intelligent Systems which has been creating and revising guidelines with the help of public input, and accepts as members many professionals from within and without its organization. The IEEE's Ethics of Autonomous Systems initiative aims to address ethical dilemmas related to decision-making and the impact on society while developing guidelines for the development and use of autonomous systems. In particular, in domains like artificial intelligence and robotics, the Foundation for Responsible Robotics is dedicated to promoting moral behavior as well as responsible robot design and use, ensuring that robots maintain moral principles and are congruent with human values.
Traditionally, government has been used by societies to ensure ethics are observed through legislation and policing. There are now many efforts by national governments, as well as transnational government and non-government organizations to ensure AI is ethically applied.
AI ethics work is structured by personal values and professional commitments, and involves constructing contextual meaning through data and algorithms. Therefore, AI ethics work needs to be incentivized.
=== Intergovernmental initiatives ===
The European Commission has a High-Level Expert Group on Artificial Intelligence. On 8 April 2019, this published its "Ethics Guidelines for Trustworthy Artificial Intelligence". The European Commission also has a Robotics and Artificial Intelligence Innovation and Excellence unit, which published a white paper on excellence and trust in artificial intelligence innovation on 19 February 2020. The European Commission also proposed the Artificial Intelligence Act, which came into force on 1 August 2024, with provisions that shall come into operation gradually over time.
The OECD established an OECD AI Policy Observatory.
In 2021, UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence, the first global standard on the ethics of AI.

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=== Governmental initiatives ===
In the United States the Obama administration put together a Roadmap for AI Policy. The Obama Administration released two prominent white papers on the future and impact of AI. In 2019 the White House through an executive memo known as the "American AI Initiative" instructed NIST (the National Institute of Standards and Technology) to begin work on Federal Engagement of AI Standards (February 2019).
In January 2020, in the United States, the Trump Administration released a draft executive order issued by the Office of Management and Budget (OMB) on "Guidance for Regulation of Artificial Intelligence Applications" ("OMB AI Memorandum"). The order emphasizes the need to invest in AI applications, boost public trust in AI, reduce barriers for usage of AI, and keep American AI technology competitive in a global market. There is a nod to the need for privacy concerns, but no further detail on enforcement. The advances of American AI technology seems to be the focus and priority. Additionally, federal entities are even encouraged to use the order to circumnavigate any state laws and regulations that a market might see as too onerous to fulfill.
The Artificial Intelligence Research, Innovation, and Accountability Act of 2024 was a proposed bipartisan bill introduced by U.S. Senator John Thune that would require websites to disclose the use of AI systems in handling interactions with users and regulate the transparency of "high-impact AI systems" by requiring that annual design and safety plans be submitted to the National Institute of Standards and Technology for oversight based on pre-defined assessment criteria.
The Computing Community Consortium (CCC) weighed in with a 100-plus page draft report – A 20-Year Community Roadmap for Artificial Intelligence Research in the US
The Center for Security and Emerging Technology advises US policymakers on the security implications of emerging technologies such as AI.
In Russia, the first-ever Russian "Codex of ethics of artificial intelligence" for business was signed in 2021. It was driven by Analytical Center for the Government of the Russian Federation together with major commercial and academic institutions such as Sberbank, Yandex, Rosatom, Higher School of Economics, Moscow Institute of Physics and Technology, ITMO University, Nanosemantics, Rostelecom, CIAN and others.
In China, the National Professional Committee on Next-Generation AI Governance issued the "Ethical Norms for the Next-Generation Artificial Intelligence" on September 25, 2021. The document outlines six basic requirements: enhancing human well-being, promoting fairness and justice, protecting privacy and safety, ensuring controllability and trustworthiness, strengthening responsibility, and improving ethical literacy. It also provides 18 specific norms for management, research and development, supply, and utilization activities. In November 2022, China submitted a "Position Paper on Strengthening the Ethical Governance of Artificial Intelligence" to the United Nations Convention on Certain Conventional Weapons (CCW) meeting. The paper advocates for the principle of "ethics first," the establishment and improvement of AI ethical rules, norms, and accountability mechanisms, and calls for the international community to reach international agreements based on broad participation.
=== Academic initiatives ===
Multiple research institutes at the University of Oxford have centrally focused on AI ethics. The Future of Humanity Institute focused on AI safety and the governance of AI before shuttering in 2024. The Institute for Ethics in AI, directed by John Tasioulas, whose primary goal, among others, is to promote AI ethics as a field proper in comparison to related applied ethics fields. The Oxford Internet Institute, directed by Luciano Floridi, focuses on the ethics of near-term AI technologies and ICTs. The AI Governance Initiative at the Oxford Martin School focuses on understanding risks from AI from technical and policy perspectives.
The Centre for Digital Governance at the Hertie School in Berlin was co-founded by Joanna Bryson to research questions of ethics and technology.
The AI Now Institute at NYU is a research institute studying the social implications of artificial intelligence. Its interdisciplinary research focuses on the themes bias and inclusion, labour and automation, rights and liberties, and safety and civil infrastructure.
The Institute for Ethics and Emerging Technologies (IEET) researches the effects of AI on unemployment, and policy.
The Institute for Ethics in Artificial Intelligence (IEAI) at the Technical University of Munich directed by Christoph Lütge conducts research across various domains such as mobility, employment, healthcare and sustainability.
Barbara J. Grosz, the Higgins Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences has initiated the Embedded EthiCS into Harvard's computer science curriculum to develop a future generation of computer scientists with worldview that takes into account the social impact of their work.
=== Private organizations ===
Algorithmic Justice League
Black in AI
Data for Black Lives

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Ethics of nanotechnology is the study of the ethical issues emerging from advances in nanotechnology and its impacts.
According to Andrew Chen, ethical concerns about nanotechnologies should include the possibility of their military applications, the dangers posed by self-replicant nanomachines, and their use for surveillance monitoring and tracking. Risks to environment to public health are treated in a report from the Dutch National Institute for Public Health and the Environment as well as is a report of the European Environment Agency. Academic works on ethics of nanotechnology can be found in the journal Nanoethics.
== Guidelines ==
According to the Markkula Center for Applied Ethics possible guidelines for an Ethics of nanotechnology could include:
Nanomachines should only be specialized, not for general purpose
Nanomachines should not be self replicating
Nanomachines should not be made to use an abundant natural compound as fuel
Nanomachines should be tagged so that they can be tracked
== Concerns ==
Ethical concern about nanotechnology include the opposition to their use to fabricate Lethal autonomous weapon, and the fear that they may self replicate ad infinitum in a so-called gray goo scenario, first imagined by K. Eric Drexler. For the EEA the challenge posed by nano-materials are due to their properties of being novel, biopersistent, readily dispersed, and bioaccumulative; by analogy, thousands cases of mesothelioma were caused by the inhalation of asbestos dust. See nanotoxicology. Nanotechnology belongs to the class of emerging technology known as GRIN: geno-, robo-, info- nano-technologies.
Another common acronym is NBIC (Nanotechnology, Biotechnology, Information Technology, and Cognitive Science). These technologies are hoped—or feared, depending on the viewpoint—to be leading to improving human bodies and functionalities (see transhumanism).
On the other hand, the possible application of nanotechnology in human genome sequencing (e.g. nanopores-based sequencing) also raises similar ethical and societal concerns.
== Further reading ==
European Environment Agency, 2013, Late lessons from early warning II Chapter 22 - Nanotechnology - early lessons from early warnings. See also Steffen et al., 2008.
Jaco Westra (editor), 2014, Assessing health and environmental risks of nanoparticles. An overview, RIVM Rapport.
Rene von Schomberg (2011), Introduction: Towards Responsible Research and Innovation in the Information and Communication Technologies and Security Technologies Fields.
R. Feynman, Cargo Cult Science, Commencement Speech at Caltech 1974. (also available in the book: Surely You're Joking, Mr. Feynman!).
European Commission, 2009, Commission recommendation on A code of conduct for responsible nanosciences and nanotechnologies research & Council conclusions on Responsible nanosciences and nanotechnologies research.
C. Marris, Final Report of the PABE research project, 2001.
E.A.J. Bleeker, S. Evertz, R.E. Geertsma, W.J.G.M. Peijnenburg, J. Westra, S.W.P. Wijnhoven, Assessing health & environmental risks of nanoparticles Current state of affairs in policy, science and areas of application, RIVM Report.
Roger Strand, 2011, Nano Ethics, In: Nanotechnology in the Agri‐Food Sector: Implications for the Future.
R. Feynman, There's Plenty of Room at the Bottom lecture given at the annual American Physical Society meeting at Caltech on December 29, 1959.
Job Timmermans; Zhao Yinghuan; and Jeroen van den Hoven, 2011. Ethics and nanopharmacy: Value sensitive design of new drugs. Nanoethics 5(3): 269–283.
Steven Umbrello and Seth D. Baum, 2018. Evaluating future nanotechnology: The net societal impacts of atomically precise manufacturing. Futures 100(June): 63–73.
K. Eric Drexler, 2013. Radical abundance: How a revolution in nanotechnology will change civilization. Public Affairs: New York.
== See also ==
Nanotechnology
Impact of nanotechnology
Molecular Manufacturing
Nanotoxicity
Nanomaterials
Nanoparticles
== References ==

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Ethics of quantification is the study of the ethical issues associated to different forms of visible or invisible forms of quantification. These could include algorithms, metrics/indicators, statistical and mathematical modelling, as noted in a review of various aspects of sociology of quantification.
According to Espeland and Stevens an ethics of quantification would naturally descend from a sociology of quantification, especially at an age where democracy, merit, participation, accountability and even ‘‘fairness’’ are assumed to be best discovered and appreciated via numbers. In his classic work Trust in Numbers Theodore M. Porter notes how numbers meet a demand for quantified objectivity, and may for this be by used by bureaucracies or institutions to gain legitimacy and epistemic authority.
For Andy Stirling of the STEPS Centre at Sussex University there is a rhetoric element around concepts such as ‘expected utility’, ‘decision theory’, ‘life cycle assessment’, ‘ecosystem services’ ‘sound scientific decisions’ and ‘evidence-based policy’. The instrumental application of these techniques and their use of quantification to deliver an impression of accuracy may raise ethical concerns.
For Sheila Jasanoff these technologies of quantification can be labeled as 'Technologies of hubris', whose function is to reassure the public while keeping the wheels of science and industry turning. The downside of the technologies of hubris is that they may generate overconfidence thanks to the appearance of exhaustivity; they can preempt a political discussion by transforming a political problem into a technical one; and remain fundamentally limited in processing what takes place outside their restricted range of assumptions.
Jasanoff contrasts technologies of hubris with 'technologies of humility' which admit the existence of ambiguity, indeterminacy and complexity, and strive to bring to the surface the ethical nature of problems. Technologies of humility are also sensitive to the need to alleviate known causes of people’s vulnerability, to pay attention to the distribution of benefits and risks, and to identify those factors and strategies which may promote or inhibit social learning.
For Sally Engle Merry, studying indicators of human rights, gender violence and sex trafficking, quantification is a technology of control, but whether it is reformist or authoritarian depends on who has harnessed its power and for what purpose. She notes in order to make indicators less misleading and distorting some principles should be followed:
democratize the production of indicators
develop in parallel qualitative research to verify the validity of assumptions
keep it the indicators simple
test or adopt multiple framings
admit the limits of the various measures
The field of algorithms and artificial intelligence is the regime of quantification where the discussion about ethics, is more advanced, see e.g. Weapons of Math Destruction of Cathy O'Neil. While objectivity and efficiency are some positive properties associated with the use of algorithms, ethical issues are posed by these tools coming in the form of black boxes. Thus algorithms have the power to act upon data and make decisions, but they are to a large extent beyond query. The existence of a surveillance capitalism in the theme of Shoshana Zuboff 2019 book. A more militant reading of the dangers posed by artificial intelligence is Resisting AI: An Anti-fascist Approach to Artificial Intelligence by Dan McQuillan.
== See also ==
Webinar at Centre for Science and Technology Studies (CWTS), Leiden University, February 5, 2021: 'Ethics of quantification' Vedeo.
Simposium on ethics of quantification Bergen (N0), December 2019
Research workshop on Ethics of quantification, Bergen (N0), December 2019
Sociology of quantification
Society for the Social Studies of Quantification - SSSQ
Special issue on Humanities and Social Sciences Communications: Ethics of Quantification: Big Data and Governing through Numbers, July 2020
Ethics in mathematics
== References ==

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The ethics of uncertain sentience is an area of applied ethics concerned with decision-making when it is unclear whether a being is sentient, meaning capable of subjective experience, feeling, or perception. The issue appears in animal ethics, especially for invertebrates such as crustaceans, cephalopods, and insects, and for fish, where the extent of their capacity to experience pain is disputed. It also appears in environmental ethics, the ethics of artificial intelligence and neuroethics.
Proposed responses include the precautionary principle, incautionary and expected-value approaches, virtue-ethical arguments for attentiveness toward possibly sentient animals, and assessment frameworks in animal welfare science. Discussion also concerns evidential standards, regulatory and economic costs, and the scope of moral consideration across biological taxa and computational substrates.
== Animal ethics ==
=== Invertebrates ===
==== Crustaceans and cephalopods ====
David Foster Wallace's 2004 essay "Consider the Lobster" describes the Maine Lobster Festival and discusses the difficulty of inferring pain across species. It notes evidence of nociceptors in lobsters alongside uncertainty about endogenous opioids, and considers the ethics of killing and cooking animals alive. Robert C. Jones's chapter "The Lobster Considered" engages Wallace's essay and argues that the available evidence supports treating lobsters as capable of pain and therefore as morally considerable. Jones reviews neurophysiological and behavioural work on nociception and opioid systems, distinguishes moral considerability from degrees of moral significance, and concludes that a precautionary approach is warranted toward practices that risk causing pain to crustaceans.
A 2021 UK government-commissioned review by the London School of Economics evaluated 300 studies and concluded that cephalopods and decapod crustaceans should be treated as sentient. It graded the evidence as "very strong" for octopods, "strong" for most crabs, and "substantial but not strong" for squid, cuttlefish, and lobsters. The review recommended best-practice transport, stunning and slaughter, and said lobsters and crabs should not be boiled alive. The review informed debate on the Animal Welfare (Sentience) Act.
==== Insects ====
In 2016, Shelley A. Adamo reviewed philosophical, neurobiological, behavioural, robotic and evolutionary evidence on insect pain and concluded that the question remains unsettled. Adamo writes that insects show nociception and complex learning, but that similar pain-like behaviours can arise from simpler mechanisms and can be engineered in robots. She argues that argument by analogy to human pain is weak without a clear account of the neural architecture needed for subjective experience. She contrasts Morgan's canon with the precautionary principle, noting that they point to opposing policy responses and that precaution has research and economic costs. She nonetheless recommends careful handling to avoid stress for methodological and ethical reasons.
In an article for Vox, Dylan Matthews discusses insect sentience in proposals to scale entomophagy. He reports limited evidence on whether farmed insects feel pain and on the welfare effects of common slaughter methods, including freezing and shredding. He cites estimates that around 1 trillion insects are raised and killed annually, with about 79–94 billion alive at any time, and argues that if insects can suffer the ethical implications of expanding insect farming would be large.
=== Fish ===
Maximilian Padden Elder argues that contemporary evidence warrants treating fish as potential sufferers within animal ethics. He distinguishes nociception from conscious pain and contends that teleosts possess nociceptors and display behaviours consistent with affective states. He describes objections based on the absence of a neocortex, or on the absence of human-like pain displays, as anthropocentric. Elder cites subcortical processing and behavioural data against neocortex-based dismissals and cautions against using human responses as the standard for other species. He also discusses cultural and psychological factors that reduce empathy for fish and lower concern for their welfare.
Given remaining uncertainty, Elder advocates a precautionary approach that shifts the burden of proof to those whose actions risk harm. He cites policy analogues including the US Marine Mammal Protection Act, UK protection of cephalopods, and European Union uses of the precautionary principle. He also points to scale as a reason for priority, noting estimates of roughly 1–2.7 trillion wild-caught fish annually and tens of billions of farmed fish slaughtered in a single year. He discusses implications for commercial and recreational fishing and questions moral pescetarianism in light of possible fish suffering.
=== Decision principles and frameworks ===
In the 2015 essay "Reconsider the Lobster", Jeff Sebo quotes Wallace's discussion of the difficulty of establishing whether an animal can experience pain. Sebo calls the question of how to treat individuals of uncertain sentience the "sentience problem" and argues that this problem, which "Wallace raises deserves much more philosophical attention than it currently receives". Sebo identifies two assumptions behind the problem: "sentientism about moral status", the view that sentient individuals deserve moral consideration, and "uncertainty about other minds", the scientific and philosophical uncertainty about which individuals are sentient.
Sebo discusses three approaches. The incautionary principle holds that in cases of uncertainty about sentience it is morally permissible to treat individuals as if they are not sentient. The precautionary principle holds that in such cases there is a moral obligation to treat them as if they are sentient. The expected value principle holds that people are "morally required to multiply our credence that they are by the amount of moral value they would have if they were, and to treat the product of this equation as the amount of moral value that they actually have". Sebo advocates the expected-value approach.
Philosopher Jonathan Birch proposes a practical framework grounded in the precautionary principle for assessing animal sentience and argues that it is consistent with established practice in animal welfare science.
Simon Knutsson and Christian Munthe argue from the perspective of virtue ethics that, in relation to animals of uncertain sentience such as "fish, invertebrates such as crustaceans, snails and insects", it is a "requirement of a morally decent (or virtuous) person that she at least pays attention to and is cautious regarding the possibly morally relevant aspects of such animals".
== Environmental ethics ==

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Kai Chan advocates an environmental ethic, a form of ethical extensionism applied to all living beings. He cites "a non-zero probability of sentience and consciousness" and argues that "we cannot justify excluding beings from consideration on the basis of uncertainty of their sentience".
== Ethics of artificial intelligence ==
Nick Bostrom and Eliezer Yudkowsky argue that if an artificial intelligence is sentient, then it is wrong to inflict unnecessary pain on it, as it is wrong to inflict pain on an animal, unless there are "sufficiently strong morally overriding reasons to do so". They also propose the "Principle of Substrate Non-Discrimination", which states: "If two beings have the same functionality and the same conscious experience, and differ only in the substrate of their implementation, then they have the same moral status."
=== AI veganism ===
AI veganism applies the rules and concepts of veganism to artificial intelligence (AI). The term has been used for the view that people should abstain from using AI because of its effects on people, animals or the environment.
Some AI vegans have compared the use of data without consent to train AI systems with harms inflicted on animals through animal husbandry. They have also compared the environmental effects of animal husbandry with those of AI training and use. On an individual level, some AI vegans argue that both consuming animal products and using AI can harm the consumer or user.
Some people avoid using large language models because they believe the training process is harmful to people or the planet.
== Neuroethics ==
Adam J. Shriver argues for "precise, precautionary, and probabilistic approaches to sentience" and states that neuroscience has different relevance to each. He concludes that basic protections for animals should be guided by the precautionary principle. He also argues that, although neuroscientific evidence is not always necessary to indicate that members of some species require protection, the "ongoing search for the neural correlates of sentience must be pursued in order to avoid harms that occur from mistaken accounts".
== See also ==
== References ==
== Further reading ==
Jakopovich, Daniel (2021). "The UK's Animal Welfare (Sentience) Bill Excludes the Vast Majority of Animals: Why We Must Expand Our Moral Circle to Include Invertebrates", Animals & Society Research Initiative, University of Victoria, Canada.
Birch, Jonathan (19 July 2024). The Edge of Sentience: Risk and Precaution in Humans, Other Animals, and AI. Oxford University Press.
Sebo, Jeff (2025-01-28). The Moral Circle: Who Matters, What Matters, and Why (A Norton Short). W. W. Norton & Company. ISBN 978-1-324-06481-7.
Clatterbuck, Hayley; Fischer, Bob (2025-01-01). "Navigating Uncertainty about Sentience". Ethics. 135 (2): 229–258. doi:10.1086/732624. ISSN 0014-1704.