让警察、学者和社区代表共同评估13种警务AI风险，发现公平性讨论反而推动了更全面的决策。
Negotiating Risk Boundaries in AI for Policing Through Mixed-Stakeholder Deliberation
- 组织30人跨群体对话，评估13个警务AI场景的风险与收益。
- 仅3个场景被直接否决，其中再犯风险评估因理念争议被拒。
- 强调种族公平能促进包容性设计，提升整体效益公平性。
AI工具在英国及全球范围内的警务中日益普及。种族偏见是已知且有充分记录的风险，但受影响社区的代表却很少参与AI采用的决策过程。本文呈现了一次混合利益相关方审议工作坊的结果，该工作坊汇集了30名社区代表、警察官员和学者，评估13个警务AI应用中的风险，特别关注种族偏见问题。研究发现，参与者总体上支持AI采纳，仅明确拒绝三个应用，尤其是再犯风险评估，其反对意见集中于概念前提而非技术实现。分析表明，突出种族公平并未缩小讨论范围，反而引导各方聚焦根本问题：该工具是否真正有效？能否带来实质性益处？这种益处是否惠及所有人？这种整合性推理方式，类似于包容性设计中的‘路缘坡效应’，凸显了从一开始就将种族偏见视角纳入AI应用的风险-收益分析的重要性。
原文摘要 · Abstract (English)
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.
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