Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing
CoRR(2024)
摘要
Research into recidivism risk prediction in the criminal legal system has
garnered significant attention from HCI, critical algorithm studies, and the
emerging field of human-AI decision-making. This study focuses on algorithmic
crime mapping, a prevalent yet underexplored form of algorithmic decision
support (ADS) in this context. We conducted experiments and follow-up
interviews with 60 participants, including community members, technical
experts, and law enforcement agents (LEAs), to explore how lived experiences,
technical knowledge, and domain expertise shape interactions with the ADS,
impacting human-AI decision-making. Surprisingly, we found that domain experts
(LEAs) often exhibited anchoring bias, readily accepting and engaging with the
first crime map presented to them. Conversely, community members and technical
experts were more inclined to engage with the tool, adjust controls, and
generate different maps. Our findings highlight that all three stakeholders
were able to provide critical feedback regarding AI design and use - community
members questioned the core motivation of the tool, technical experts drew
attention to the elastic nature of data science practice, and LEAs suggested
redesign pathways such that the tool could complement their domain expertise.
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