The Ethics of AI Ethics -- An Evaluation of Guidelines (1903.03425v2)

Abstract: Current advances in research, development and application of AI systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems - and how the effectiveness in the demands of AI ethics can be improved.

• The paper evaluates 21 AI ethics guidelines, revealing that despite their comprehensive coverage, they have negligible impact on real-world AI decision-making.
• The analysis uncovers recurring themes like accountability, privacy, and fairness while highlighting omissions such as existential risks and political misuse.
• The study recommends combining technical precision with virtue ethics to develop guidelines that effectively steer ethical AI practices.

The Ethics of AI Ethics: An Evaluation of Guidelines

Author: Dr. Thilo Hagendorff (University of Tuebingen, International Center for Ethics in the Sciences and Humanities)

Abstract: This paper meticulously scrutinizes 21 major AI ethics guidelines, setting off to uncover their overlaps, omissions, and overall effectiveness in their purported role of directing the ethical development, deployment, and use of AI systems. The central thesis posited is that though these guidelines are abundant and collectively comprehensive, they largely remain ineffectual in practical scenarios, suggesting that significant gaps and inherent weaknesses persist in the domain of AI ethics.

Introduction and Rationale

The paper embarks on its analytical journey by noting the proliferation of ethics guidelines intended to mitigate the potentially disruptive impact of AI technologies. Dr. Hagendorff poses a critical question: do these guidelines tangibly influence decision-making processes in AI research, development, and application? His analysis, addressing 21 prominent AI ethics guidelines, reveals a robust “no” as the prevailing answer. The guidelines often emerge more as aspirational documents than binding operational mandates capable of steering concrete ethical practices in AI endeavors.

Key Findings

Dr. Hagendorff identifies several recurrent themes across the examined guidelines:

1. Common Ethical Issues Covered: Accountability, privacy protection, fairness and non-discrimination, transparency, and safety appear in over 70% of the guidelines.
2. Technical Areas of Research: Ethical considerations often find their resolution through technical implementations, notably in fields like accountability, fairness, and privacy.
3. Corporate Influence and Self-Governance: Industry-driven guidelines frequently posit that internal governance and self-commitment are sufficient, thus staving off external regulatory measures. This approach turns ethics into a tool for public relations rather than a practical framework for ethical AI.

Omissions in AI Ethics Guidelines

The analysis points out several critical ethical issues that are conspicuously absent or significantly underrepresented in these guidelines:

• Malevolent AI and Existential Risks: The potential dangers posed by a malevolent AGI and other existential threats are not addressed.
• Political Misuse and Social Impact: Issues such as the political abuse of AI systems (e.g., automated propaganda, manipulation via social media), social sorting, and the impacts on social cohesion are largely neglected.

The Effectiveness of AI Ethics in Practice

A remarkable insight presented is the negligible impact ethics guidelines have on the decision-making behaviors of AI practitioners. Empirical research corroborated within the paper suggests that even when software engineers are exposed to these guidelines, their decision-making processes remain largely unchanged.

Methodological Approach and Theoretical Insights

Dr. Hagendorff employs a comprehensive literature analysis, comparing guidelines from governmental, industrial, and scientific domains. He critiques both the substance of these guidelines and their broader socio-political functions. A crucial observation is that modern ethical discourse around AI, influenced predominantly by male-dominated technical communities, focuses more on abstract, calculative justice ethics rather than contextual, empathy-oriented care ethics.

Implications and Recommendations

Theoretical Implications: Theoretically, the paper suggests that there is a disconnect between high-level ethical principles and their practical implementation. Dr. Hagendorff advocates for an ethics framework that is not only deontological but also encompasses virtue ethics to promote personal responsibility and practical ethical decision-making.

Practical Recommendations: The suggested improvements include the introduction of more detailed, technically grounded instructions in ethics guidelines, the adoption of legal frameworks and independent auditing mechanisms, and the significance of education in ethics integrated within AI curricula.

Conclusion

The paper concludes with a reflection on the current state of AI ethics, highlighting its shortcomings and outlining a roadmap for a more robust and actionable ethical framework. By drawing from both deontological and virtue ethics perspectives, Dr. Hagendorff emphasizes the need for a balanced approach that integrates rigorous technical specifications with broader humanistic values to effectively guide ethical AI development.

Keywords: artificial intelligence, machine learning, ethics, guidelines, implementation