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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Ethics of artificial intelligence | 5/12 | https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence | reference | science, encyclopedia | 2026-05-05T06:58:46.886169+00:00 | kb-cron |
=== 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 ===