The use of principles in human research ethics is well established (though which principles and how they’re used in different disciplines and cultural settings has been debated), but do they work for AI? This open access paper reflects on this question. #SpoilerAlert it suggests not.
Artificial intelligence (AI) ethics is now a global topic of discussion in academic and policy circles. At least 84 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 for 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.
Mittelstadt, B. (2018) Principles alone cannot guarantee ethical AI. Nature Machine Intelligence 1, 501–507. https://doi.org/10.1038/s42256-019-0114-4
Publisher (Open Access): https://www.nature.com/articles/s42256-019-0114-4
