The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions

By Jess Whittlestone, Rune Nyrup, Anna Alexandrova, Stephen Cave. 2019

348 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Safety Research, AI Ethics, Fairness & Society, Philosophy

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Citations in the corpus

Release Strategies and the Social Impacts of Language Models (2019, background): “[65] on the need to focus on tensions between principles in order to make progress on AI ethics.”
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, background): “Further, they are amenable to various interpretations [13][14] and agreement on them can mask deeper disagreements [5].”
The relationship between trust in AI and trustworthy machine learning technologies (2019, background): “es of the involved science and technology, but do not go into the specics of technical implementation guidelines. There is emerging literature reviewing Principled AI frameworks. Whittlestone et al. [88] provide a critical analysis of frameworks for ethical machine learning and highlights a number o”
ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles (2019, background): “Although study after study conﬁrms that meaningful progress cannot be made until ethical ideals are operationalized [17, 12, 5], the inconsistency with which high level principles such as transparency are interpreted across different contexts, organizations and even teams makes it difﬁcult to…”
Auditing large language models: a three-layered approach (2023, background): “Bringing all this together, our blueprint for how to audit LLMs focuses on making implicit choices and tensions visible, giving voice to different stakeholders, and generating resolutions that – even when imperfect – are, at least, more explicit and publicly defensible [106].”