AI 中文总结
该研究通过包含社区代表、警察和学者的混合利益相关者协商研讨会，评估13个警务AI用例的种族偏见风险，发现多数参与者支持AI采用，仅拒绝3个用例，强调种族公平可优化AI风险-收益分析。
AI 中文摘要
英国及全球各地警务领域正日益采用人工智能工具，种族偏见是已知且被充分记录的风险，但受影响社区的代表很少被纳入AI采用决策。我们展示了一场混合利益相关者协商研讨会的结果，该研讨会汇集了30名社区代表、警察和学者，以评估13个警务AI用例的风险，重点关注种族偏见。我们发现参与者总体上对AI采用持开放态度，仅明确拒绝3个用例，最值得注意的是累犯风险评估，反对意见针对的是前提而非实施。我们的分析显示，强调种族公平并未缩小协商范围，相反，讨论聚焦于一系列基本问题：该工具是否真正有效、是否会带来切实益处、该益处是否会惠及所有人。这种综合推理让人联想到包容性设计中的 curb-cut 效应（坡道效应），凸显了从一开始就将种族偏见视角纳入AI用例的风险-收益分析的益处。
英文摘要
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.
CommentsAccepted to AIES 2026
