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Beyond the principle-practice gap: Responsible AI as situated epistemic and ethical practice

This paper challenges the common framing of the gap between AI ethics principles and practice as a mere implementation or compliance deficit. Based on interviews with actors in AI-enabled anti-trafficking systems, it argues that ethical concerns are shaped by institutional and professional contexts, and that epistemic choices are intertwined with ethical judgments. The authors propose a model of four moments—perception, judgment, situated action, and socio-technical materialization—through which concerns become actionable or are deferred. They conclude that responsible AI governance requires epistemic infrastructures to make affected communities' knowledge receivable in design.

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Provides a nuanced, evidence-based perspective on responsible AI implementation, useful for researchers and practitioners addressing ethics in practice.

arXiv:2611.06709v1 Announce Type: cross Abstract: Responsible AI scholarship has found that ethical principles are frequently not reflected in AI design and development. Conceptualised as a `gap' between principle and practice, this is framed as a problem of implementation, awareness or compliance, and by implication, a deficit of systems or individual behaviour. This paper sets out an alternative explanation. Drawing on fourteen semi-structured interviews with actors designing and governing AI-enabled anti-trafficking systems across law enforcement, commercial, research, and survivor-support contexts, it argues that such apparent gaps emerge from the varied and complex institutional and professional conditions through which actors come to recognise some issues, but not others, as ethical problems requiring action. We show that epistemic choices are intertwined with ethical judgements, and trace four interconnected moments---perception, judgement, situated action and socio-technical materialisation---through which concerns become intelligible, are weighed and acted upon, and are embedded in technical artefacts and governance arrangements. Actors often hold genuine ethical commitments, but these commitments are shaped by institutional logics and professional moral economies: some concerns are materialised, while others are narrowed, deferred or left unrealised. We show that ethical agency is exercised within and between competing constraints of profession, role, prior experience, and institutional settings. The paper argues that responsible AI governance requires purposeful and well-developed epistemic infrastructures that can cut across these constraints to establish institutional conditions through which the knowledge of affected communities and potential harms becomes receivable and actionable in design.
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