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Table of Contents
1. Abstract
2. Introduction
3. The challenges of a principled approach to AI Ethics
3.1 Common aims and fiduciary duties
3.2 Professional history and norms
Summary
AI Ethics is a global topic of discussion with various initiatives proposing high-level principles for ethical development and governance. Recent meta-analyses suggest convergence on principles similar to medical ethics. However, differences between AI and traditional professions raise concerns about the effectiveness of a principled approach. AI lacks common aims, professional history, proven methods, and robust accountability mechanisms, making ethical decision-making challenging. The absence of a fiduciary relationship in AI raises questions about users' trust in developers' ethical implementation. Unlike medicine, AI lacks a well-defined professional culture and history of 'good' behavior. Medical ethics have evolved over time, responding to failures and setting standards for practitioners. In contrast, the field of AI development lacks a unified professional identity and ethics framework. The complexity and unpredictability of AI systems pose challenges in defining 'good' practices for developers. Current AI Ethics initiatives offer high-level principles but lack specific guidance for implementation, leading to vague and abstract ethical requirements. The ambiguity in concepts like 'fairness' and 'dignity' masks underlying ethical conflicts and impedes the translation of principles into practice. The absence of consensus on practical directions for ethical AI development highlights the need for a clearer roadmap and unified implementation strategies.