Beyond the principle-practice gap: Responsible AI as situated epistemic and ethical practice
Organizations: University of Bath, Bath, United Kingdom
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.
Figures & tables
| Analytical moment | How concerns are lost or weakened | Illustrative accounts | Governance implications |
|---|---|---|---|
| Perception | Concerns remain unrecognised or insufficiently articulated within the actor’s professional or institutional understanding. | A — limited recognition of profiling harms | Bring alternative forms of knowledge and institutional perspectives into problem formulation, including affected-community perspectives; create opportunities to question prevailing assumptions. |
| Judgement | Concerns are recognised but outweighed by competing priorities, without clear justification of the trade-offs. | E — privacy versus detection; H — worker harm versus commercial objectives | Make trade-offs explicit and open to scrutiny: what concerns were considered, whose interests were prioritised, who made the decision and on what grounds. |
| Situated action | Concerns are judged important but actors lack the resources, authority or mandate to act, or responsibility is displaced to other actors. | C, D — institutional constraints; E, L — displaced responsibility | Provide resources, decision-making authority and institutional support; clarify responsibilities across development, deployment and downstream use. |
| Socio-technical materialisation | Concerns inform development but are only partially incorporated, narrowed, left undeployed or not sustained over time. | E, C, K, D, F | Trace how ethical judgements are translated into design and deployment; review what was incorporated, modified or excluded; establish responsibility, resources and continuity for implementation and maintenance. |
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
| Code | Institutional setting | Professional role | Type of application discussed |
|---|---|---|---|
| A | Law enforcement/start-up | Practitioner with law-enforcement and investigative experience | Data-driven investigation linking administrative and financial records |
| B | Research institute | Researcher (computing) working with frontline organisations | Worker-facing screening and support tool |
| C | Academia | Researcher | Automated analysis of corporate modern slavery reporting |
| D | Academia | Researcher | Forced-labour risk assessment in a maritime setting |
| E | Private sector | Investigative technology developer | Online and open-source intelligence analysis |
| F | Civil society and technology development | Data and AI developer with prior NGO experience | Data tools for NGOs and survivor-support organisations |