AI Ethics - Too Principled to Fail?

By Brent Daniel Mittelstadt. 2019

89 citations recorded by the source corpus.

AI Ethics is now a global topic of discussion in academic and policy circles. At least 63 public-private initiatives have produced statements describing high-level principles, values, and other tenets to guide the ethical development, deployment, and governance of AI. According to recent meta-analyses, AI Ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI Ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach in the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement.

Subfields: Safety Research

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Citations in the corpus

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, neutral): “Over 80 AI organizations [5], including technology companies such as Google [22], OpenAI [23], and Microsoft [24] have publicly stated the principles they will follow in developing AI.”
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, positive): “However, the AI industry lacks proven methods to translate principles into practice [49], and AI principles have been criticized for being vague and providing little to no means of accountability [27, 82].”
Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI (2021, unknown): “In many cases, ethical design was considered after the technology is developed rather than during development (Mittelstadt, 2019).”
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, unknown): “They encourage more rigorous analysis of social and ethical considerations [135], especially when operationalizing principles seems opaque [147].”
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI (2020, unknown): “Moreover, differing assumptions or interpretations can yield conﬂicting operationalizations—a challenge that can be masked when principles are left at a high level [68].”
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020, unknown): “While principlism is not the only available bioethical framework, it is a very useful basic practical framework with high acceptance both in research and medical settings [36–38].”
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction (2022, background): “They encourage more rigorous analysis of social and ethical considerations [116], especially when operationalizing principles feels opaque [128].”
The global landscape of AI ethics guidelines (2019, unknown): “Such efforts can be mediated and facilitated by intergovernmental organizations, complemented by bottom-up approaches involving all relevant stakeholders 135 .”