Artificial Intelligence and the ethics of navigating ambiguity

Bennett, SJ

Abstract

This paper examines ambiguity within AI practice, arguing for an ethics of AI which stays with fundamental ambiguities and accounts for their complex socio-material entanglements. However, common approaches to responsible governance of AI are often predicated upon notions of predictable pipelines and static outputs which are assumed to be easily describable and cleanly structured. Drawing upon empirical findings which challenge these notions, and conceptual tools from Simone de Beauvoir's Ethics of Ambiguity, I illustrate how AI [ethics] can be better understood as grounded in ambiguity and propose reframing ambiguity from a failure or risk to a core facet of the study and governance of AI. I report on interviews with 23 AI practitioners, combined with observations from an ethnography of an AI practitioner based in an industry AI lab, examining their motivations, aims and actions in developing and implementing AI models. Practitioners described the impact of local, epistemic and systemic constraints, employing heuristics, intuition and creative problem-solving to navigate embedded, inherent ambiguity and uncertainty across material and practice. Building on these analyses, I propose that engaging with ambiguity in the study and ethics of AI can provide productive sites for ethical reflection and governance.

Citation

Bennett, S. (2025). Artificial Intelligence and the ethics of navigating ambiguity. Big Data & Society, 12(2), https://doi.org/10.1177/20539517251347594

• Journal Article Type: Article
• Acceptance Date: May 30, 2025
• Online Publication Date: Jun 30, 2025
• Publication Date: 2025-06
• Deposit Date: Jul 8, 2025
• Publicly Available Date: Jul 8, 2025
• Journal: Big Data & Society
• Electronic ISSN: 2053-9517
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 2
• DOI: https://doi.org/10.1177/20539517251347594
• Keywords: Artificial Intelligence, ethics, ambiguity, socio-technical systems, data work
• Public URL: https://durham-repository.worktribe.com/output/4254282