---
title: "Machine ethics"
chunk: 6/7
source: "https://en.wikipedia.org/wiki/Machine_ethics"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T04:23:37.276938+00:00"
instance: "kb-cron"
---

=== Trust, trustworthiness, and AI ===
Recent philosophical work on trust in artificial intelligence has distinguished the various attitudes users can have toward AI systems. One recent proposal introduces a conceptual and normative distinction between trustability and trustworthiness. Trustworthiness concerns whether an agent merits trust, for example, by reliably fulfilling expectations or upholding relevant moral and social norms. Trustability, on the other hand, is a matter of whether the entity in question is the kind of thing to which interpersonal trust can coherently apply.
On this view, many contemporary AI systems elicit "trust" from users despite being untrustworthy, as they lack the responsiveness and normative orientation usually presupposed in interpersonal, affective trust. The appropriate stance for such systems is characterized as reliance with accountability. Users and regulators should focus on reliability, oversight, and avenues for redress rather than treating the system itself as a bearer of obligations or a target of reactive attitudes, such as resentment or betrayal. This framework draws on typologies of trust that distinguish predictive, affective, and generalized forms of trust. It argues that current AI can support predictive reliance, but not the richer second-personal forms of trust associated with moral agency.
Related philosophical analyses emphasize that trust in AI should not be reduced to technical reliability or user confidence. Durán and Pozzi argue that trust involves an irreducibly normative dimension that goes beyond successful performance, requiring responsiveness to expectations, accountability, and the possibility of justified complaint. Many contemporary discussions of "trustworthy AI" conflate trust with reliability or transparency, obscuring that genuine trust presupposes forms of responsibility and normative commitment that current AI systems lack. This supports approaches that prioritize institutional and governance-based mechanisms for managing reliance on AI rather than encouraging interpersonal trust attitudes toward artificial agents.
At the same time, the literature explores whether future AI systems could become trustable and trustworthy if they acquired forms of artificial agency that could recognize others' dependence and treat it as normatively significant in their practical deliberations. Some authors discuss institutional surrogate trust, in which trust is placed in the surrounding institutions, governance structures, and recourse mechanisms that ensure responsiveness and accountability rather than in the AI system itself. This approach connects discussions about "trustworthy AI" to larger questions about designing socio-technical systems and regulatory frameworks and calibrating human attitudes toward nonhuman agents.

== Ethical frameworks and practices ==

=== Practices ===
In March 2018, in an effort to address rising concerns over machine learning's impact on human rights, the World Economic Forum and Global Future Council on Human Rights published a white paper with detailed recommendations on how best to prevent discriminatory outcomes in machine learning. The World Economic Forum developed four recommendations based on the UN Guiding Principles of Human Rights to help address and prevent discriminatory outcomes in machine learning:

Active inclusion: Development and design of machine learning applications must actively seek a diversity of input, especially of the norms and values of populations affected by the output of AI systems.
Fairness: People involved in conceptualizing, developing, and implementing machine learning systems should consider which definition of fairness best applies to their context and application, and prioritize it in the machine learning system's architecture and evaluation metrics.
Right to understanding: Involvement of machine learning systems in decision-making that affects individual rights must be disclosed, and the systems must be able to explain their decision-making in a way that is understandable to end users and reviewable by a competent human authority. Where this is impossible and rights are at stake, leaders in the design, deployment, and regulation of machine learning technology must question whether it should be used.
Access to redress: Leaders, designers, and developers of machine learning systems are responsible for identifying the potential negative human rights impacts of their systems. They must make visible avenues for redress for those affected by disparate impacts, and establish processes for the timely redress of any discriminatory outputs.
In January 2020, Harvard University's Berkman Klein Center for Internet and Society published a meta-study of 36 prominent sets of principles for AI, identifying eight key themes: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. Researchers at the Swiss Federal Institute of Technology in Zurich conducted a similar meta-study in 2019.