Principles alone cannot guarantee ethical AI

TL;DR

This paper argues that relying solely on high-level principles is insufficient for ensuring ethical AI, due to fundamental differences between AI development and medicine that hinder effective implementation and accountability.

Contribution

It critically examines the limitations of principle-based approaches in AI ethics, highlighting key differences from medicine that challenge their effectiveness.

Findings

Principle-based AI ethics lacks clear implementation methods.

Differences from medicine undermine the success of principles in AI.

High-level consensus may mask deep political and normative disagreements.

Abstract

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 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.