为执法领域AI决策系统设计提供用户需求指南
Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcement
- 通过实地调研识别执法者对AI系统的数据处理与可解释性需求
- 强调人类需参与数据审查与输出验证以保障系统可信度
- 适合关注执法AI人机协同的政策制定者与系统设计师
人工智能已深度融入日常生活,但执法领域AI辅助系统的设计仍缺乏明确的用户需求。本研究通过质性方法考察执法机构的决策过程,旨在识别现有实践的局限、探索用户需求,并理解人类在系统中期望承担的责任。参与者普遍认为系统需高效处理海量数据以支持犯罪侦测与预防,同时应具备可扩展性、准确性、可解释性、可信度及适应性。用户强调必须审查可能难以被AI解读的输入数据,并验证输出结果以确保准确。为应对执法环境的动态变化,用户需协助系统适应犯罪行为与政策调整,技术专家则需持续监控。友好的人机交互是系统采纳的关键,部分参与者表示愿持续反馈以帮助系统学习。最后指出,由于执法领域的复杂性,完全自动化几乎不可能实现。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) has become an important part of our everyday lives, yet user requirements for designing AI-assisted systems in law enforcement remain unclear. To address this gap, we conducted qualitative research on decision-making within a law enforcement agency. Our study aimed to identify limitations of existing practices, explore user requirements and understand the responsibilities that humans expect to undertake in these systems. Participants in our study highlighted the need for a system capable of processing and analysing large volumes of data efficiently to help in crime detection and prevention. Additionally, the system should satisfy requirements for scalability, accuracy, justification, trustworthiness and adaptability to be adopted in this domain. Participants also emphasised the importance of having end users review the input data that might be challenging for AI to interpret, and validate the generated output to ensure the system's accuracy. To keep up with the evolving nature of the law enforcement domain, end users need to help the system adapt to the changes in criminal behaviour and government guidance, and technical experts need to regularly oversee and monitor the system. Furthermore, user-friendly human interaction with the system is essential for its adoption and some of the participants confirmed they would be happy to be in the loop and provide necessary feedback that the system can learn from. Finally, we argue that it is very unlikely that the system will ever achieve full automation due to the dynamic and complex nature of the law enforcement domain.
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