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.
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.
| Original language | English |
|---|---|
| Journal | Evaluation and Assessment in Software Engineering |
| Publication status | Acceptance date - 26 Mar 2025 |
