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

arXiv: https://arxiv.org/abs/2504.17393 · HTML: https://mlsanity.com/paper/2504.17393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization](https://mlsanity.com/paper/2308.05487/) (2023)
- [Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy](https://mlsanity.com/paper/2008.12735/) (2020)
- [Teaching Inverse Reinforcement Learners via Features and Demonstrations](https://mlsanity.com/paper/1810.08926/) (2018)
- [Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry](https://mlsanity.com/paper/2109.00872/) (2021)
- [Requirement Engineering Challenges for AI-intense Systems Development](https://mlsanity.com/paper/2103.10270/) (2021)
- [Human-in-the-Loop Design Cycles -- A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans](https://mlsanity.com/paper/2003.05268/) (2020)
- [User-Centric Deployment of Automated Program Repair at Bloomberg](https://mlsanity.com/paper/2311.10516/) (2023)
- [Trustworthy AI in practice: an analysis of practitioners' needs and challenges](https://mlsanity.com/paper/2407.12135/) (2024)

## Abstract (arXiv)

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.