BAYERL, Petra and AKHGAR, Babak (2026). Establishing AI Accountability in Policing and Security. In: AKHGAR, Babak and BAYERL, Petra, (eds.) Accountability for Artificial Intelligence in Policing and Security. Security Informatics and Law Enforcement . Cham, Springer, 71-84. [Book Section]
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Bayerl-EstablishingAIAccountability(VoR).pdf - Published Version
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Bayerl-EstablishingAIAccountability(VoR).pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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Abstract
Artificial Intelligence (AI) presents policing with important capabilities to increase the speed, accuracy, and range of investigations whilst reducing costs and potentially directing resources more effectively and efficiently. Yet the frequent challenge police organisations face is how to capitalize on AI for the better protection of society whilst also reacting to legitimate societal concern about its use. AI accountability imposes obligations on organisations to justify their AI usage, monitor its application, and above all, enforce changes and redress if things go wrong. We argue that it is therefore a practical instrument not only for the proactive risk assessment and management but also to understand the benefits of AI deployment. This chapter offers the conceptual foundation for AI accountability in policing. It defines AI accountability as a composite of 12 accountability principles. It further explains how to make the concept practical and assessable through the operationalisation of the principles into eight themes.
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