Negotiating Risk Boundaries in AI for Policing Through Mixed-Stakeholder Deliberation

2026-08-05

AI Summary

This study addresses fairness risks in current AI applications in policing, which often arise from the exclusion of communities disproportionately affected by racial bias. By convening 30 community members, police officers, and scholars in a mixed-stakeholder deliberative workshop, the research conducts a systematic risk–benefit analysis of 13 AI use cases in law enforcement, uniquely integrating a racial equity lens into the evaluation framework from the outset. Combining qualitative dialogue with structured risk assessment, the findings reveal that inclusive deliberation effectively steers participants toward prioritizing social efficacy and equitable impact over mere technical feasibility. While most use cases were broadly accepted, participants explicitly rejected three high-risk applications, including recidivism risk assessment. The deliberative process exhibited “curb-cut effect”–style integrative reasoning, fostering a more equitable and consensus-driven pathway for AI governance.

Abstract

AI tools are being increasingly adopted in policing in the UK and worldwide. Racial bias is a known and well-documented risk, yet representatives of affected communities are rarely included in decisions about AI adoption. We present results from a mixed-stakeholder deliberation workshop bringing together 30 community representatives, police officers, and academics to assess the risks of 13 AI use cases in policing, with an explicit focus on racial bias. We found that participants were broadly open to AI adoption, rejecting only three use cases outright, most notably recidivism risk assessment, where objections targeted the premise rather than the implementation. Our analysis reveals that foregrounding racial equity did not narrow the deliberation. Instead, discussions gravitated toward a fundamental set of questions: does this tool actually work, will it deliver genuine benefit, and will that benefit extend to everyone? This integrated reasoning, reminiscent of the curb-cut effect in inclusive design, highlights the benefit of incorporating the racial bias lens into the risk-benefit analysis of AI use cases from the outset.

Problem

• racial bias
• AI in policing
• stakeholder deliberation
• risk assessment
• racial equity

Innovation

• mixed-stakeholder deliberation
• racial bias
• AI in policing
• inclusive design
• risk-benefit analysis