The Role and Limits of Principles in AI Ethics
Towards a Focus on Tensions
Bibliographic Data
| ID | 23360354 |
|---|---|
| Authors | Jess Whittlestone (0000-0002-1252-718X, University of Cambridge), Rune Nyrup (0000-0002-9880-6912, University of Cambridge), Anna Alexandrova (0000-0002-3226-6044, University of Cambridge), Syto Cavé (0000-0002-0764-0874, University of Cambridge), Stephen Cave (University of Cambridge, Cambridge, United Kingdom) |
| Year | 2019 |
| Pages | 195-200 |
| Publication date | 2019-01-27 |
| Open Access | Yes |
| Type | CONFERENCE |
| Venue | Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (CONFERENCE) |
| Publisher | ACM (PUBLISHER) |
| DOI | 10.1145/3306618.3314289 |
| OpenAlex | W2956792022 |
| Language | EN |
| Citations received | 54 |
| References cited | 13 |
The last few years have seen a proliferation of principles for AI ethics. There is substantial overlap between different sets of principles, with widespread agreement that AI should be used for the common good, should not be used to harm people or undermine their rights, and should respect widely held values such as fairness, privacy, and autonomy. While articulating and agreeing on principles is important, it is only a starting point. Drawing on comparisons with the field of bioethics, we highlight some of the limitations of principles: in particular, they are often too broad and high-level to guide ethics in practice. We suggest that an important next step for the field of AI ethics is to focus on exploring the tensions that inevitably arise as we try to implement principles in practice. By explicitly recognising these tensions we can begin to make decisions about how they should be resolved in specific cases, and develop frameworks and guidelines for AI ethics that are rigorous and practically relevant. We discuss some different specific ways that tensions arise in AI ethics, and what processes might be needed to resolve them.
Applied ethics · Autonomy · Bioethics · Engineering ethics · Epistemology · Ethics of technology · Field (mathematics) · Focus (optics) · Harm · Information ethics · Management science · Meta-ethics · Political science · Sociology · Computer Science · Engineering · Ethics and Social Impacts of AI · Law · Mathematics · Neuroethics, Human Enhancement, Biomedical Innovations · Philosophy · Psychology of Moral and Emotional Judgment
The 3×2A framework
Operationalising AI ethics
Collective action on artificial intelligence
A Leap of Faith
Closing the AI accountability gap
The global landscape of AI ethics guidelines
Operationalising AI governance through ethics-based auditing
Another View on Article 22 GDPR
Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions
Operationalizing Pluralist AI Governance with the Integrated Axiology–MCDA Framework
Large AI Models Have a Prioritization Problem
AI Ethics
Accounting for Personalization in Personalization Algorithms
Dual-use implications of AI text generation
What Do We Teach to Engineering Students
Saf
Ethics-Based Auditing of Automated Decision-Making Systems
AI ethical challenges
Technology ethics assessment
A Framework for Evaluating Global AI Governance Initiatives
AI governance
Imperfection as a constitutive property of artificial intelligence
Organisational tensions in introducing socially sustainable AI
Examining trends in AI ethics across countries and institutions via quantitative discourse analysis
Competing narratives in AI ethics
Operationalising AI ethics
Challenges of responsible AI in practice
Understanding AI and power
Ethical governance of artificial intelligence for defence
Beyond ideals
The representation problem in AI ethics
Missed opportunities for AI governance
What does it mean to embed ethics in data science? An integrative approach based on microethics and virtues
Rethinking agency in AI ethics
Artificial intelligence and economic psychology
Investing in AI for social good
Online public discourse on artificial intelligence and ethics in China
Artificial intelligence ethics has a black box problem
Technologies of the collective
Responsible scaling of artificial intelligence in healthcare
From AI Ethics Principles to Practices
Contextual Integrity as a General Conceptual Tool for Evaluating Technological Change
In Defence of Principlism in AI Ethics and Governance
A Virtue-Based Framework to Support Putting AI Ethics into Practice
Ethics in the Software Development Process
AI ethics in postcolonial contexts
Governing artificial intelligence in China and the European Union
Ethical reasoning in artificial intelligence
The tragedy of the AI commons
Disagreement, AI alignment, and bargaining
Solving AI ethics? Hybrid expertise and professional power in EU ethics governance
Addressing ethical gaps in 'Technology for Good
Ethics and Artificial Intelligence in the Interdisciplinary Collaborations of Smart Care
Faithful Innovation
| Citations per year | 7.71 |
|---|---|
| Citation span | 2019 - 2026 (8) |
| Highly cited | No |