Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcement

TL;DR

The paper addresses how to design AI-assisted decision-making in law enforcement through a user-centered lens. It employs qualitative interviews with 12 investigators and thematic analysis to elicit 827 unique requirements, highlighting key non-functional attributes such as scalability, accuracy, justification, trustworthiness, and adaptability, together with explicit human-in-the-loop responsibilities. A human-in-the-loop architecture is proposed, featuring explainable outputs, regular validation, data governance, and continuous monitoring to reconcile AI capabilities with evolving domain guidance. The study argues that full automation is unlikely in policing due to the domain's complexity, and provides actionable design guidance for developers and policymakers to enable safer, more effective AI adoption in LEAs.

Abstract

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.

Figures (1)

• Figure 1: The human-in-the-loop AI-assisted decision-making system that achieves user requirements for scalability, accuracy, justification, trustworthiness and adaptability.