AI in Government: A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service

Fabian Walke; Lars Bennek; Till J. Winkler

doi:10.1007/978-3-031-80125-9_23

AI in Government: A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service

Fabian Walke^*, Lars Bennek, Till J. Winkler

^*Corresponding author for this work

Department of Digitalization

University of Hagen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Law enforcement and police service are, related to the proposed AI Act of the European Commission, part of the high-risk area of artificial intelligence (AI). As such, in the area of digital government and high-risk AI systems exists a particular responsibility for ensuring ethical and social aspects with AI usage. The AI Act also imposes explainability requirements on AI, which could be met by the usage of explainable AI (XAI). The literature has not yet addressed the characteristics of the high-risk area law enforcement and police service in relation to compliance with explainability requirements. We conducted 11 expert interviews and used the grounded theory method to develop a grounded model of the phenomenon AI explainability requirements compliance in the context of law enforcement and police service. We discuss how the model and the results can be useful to authorities, governments, practitioners and researchers alike.

Original language	English
Title of host publication	Transforming the Digitally Sustainable Enterprise : Proceedings of the 18th International Conference on Wirtschaftsinformatik, Paderborn, Germany, 2023, Vol. 3
Editors	Daniel Beverungen, Christiane Lehrer, Matthias Trier
Number of pages	15
Place of Publication	Cham
Publisher	Springer
Publication date	Mar 2025
Pages	393-407
ISBN (Print)	9783031801242
ISBN (Electronic)	9783031801259
DOIs	https://doi.org/10.1007/978-3-031-80125-9_23
Publication status	Published - Mar 2025
Event	18th International Conference on Wirtschaftsinformatik, WI 2023 - Universität Paderborn, Paderborn, Germany Duration: 18 Sept 2023 → 21 Sept 2023 Conference number: 18 https://wi2023.de/en/startseite-english/

Conference

Conference	18th International Conference on Wirtschaftsinformatik, WI 2023
Number	18
Location	Universität Paderborn
Country/Territory	Germany
City	Paderborn
Period	18/09/2023 → 21/09/2023
Internet address	https://wi2023.de/en/startseite-english/

Series	Lecture Notes in Information Systems and Organisation
Volume	76
ISSN	2195-4968

Keywords

XAI
Artificial intelligence
AI act
Requirements
Compliance

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Cite this

Walke, F., Bennek, L., & Winkler, T. J. (2025). AI in Government: A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service. In D. Beverungen, C. Lehrer, & M. Trier (Eds.), Transforming the Digitally Sustainable Enterprise: Proceedings of the 18th International Conference on Wirtschaftsinformatik, Paderborn, Germany, 2023, Vol. 3 (pp. 393-407). Springer. https://doi.org/10.1007/978-3-031-80125-9_23

Walke, Fabian ; Bennek, Lars ; Winkler, Till J. / AI in Government : A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service. Transforming the Digitally Sustainable Enterprise: Proceedings of the 18th International Conference on Wirtschaftsinformatik, Paderborn, Germany, 2023, Vol. 3. editor / Daniel Beverungen ; Christiane Lehrer ; Matthias Trier. Cham : Springer, 2025. pp. 393-407 (Lecture Notes in Information Systems and Organisation, Vol. 76).

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abstract = "Law enforcement and police service are, related to the proposed AI Act of the European Commission, part of the high-risk area of artificial intelligence (AI). As such, in the area of digital government and high-risk AI systems exists a particular responsibility for ensuring ethical and social aspects with AI usage. The AI Act also imposes explainability requirements on AI, which could be met by the usage of explainable AI (XAI). The literature has not yet addressed the characteristics of the high-risk area law enforcement and police service in relation to compliance with explainability requirements. We conducted 11 expert interviews and used the grounded theory method to develop a grounded model of the phenomenon AI explainability requirements compliance in the context of law enforcement and police service. We discuss how the model and the results can be useful to authorities, governments, practitioners and researchers alike.",

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Walke, F, Bennek, L & Winkler, TJ 2025, AI in Government: A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service. in D Beverungen, C Lehrer & M Trier (eds), Transforming the Digitally Sustainable Enterprise: Proceedings of the 18th International Conference on Wirtschaftsinformatik, Paderborn, Germany, 2023, Vol. 3. Springer, Cham, Lecture Notes in Information Systems and Organisation, vol. 76, pp. 393-407, 18th International Conference on Wirtschaftsinformatik, WI 2023, Paderborn, Germany, 18/09/2023. https://doi.org/10.1007/978-3-031-80125-9_23

AI in Government: A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service. / Walke, Fabian; Bennek, Lars; Winkler, Till J.
Transforming the Digitally Sustainable Enterprise: Proceedings of the 18th International Conference on Wirtschaftsinformatik, Paderborn, Germany, 2023, Vol. 3. ed. / Daniel Beverungen; Christiane Lehrer; Matthias Trier. Cham: Springer, 2025. p. 393-407 (Lecture Notes in Information Systems and Organisation, Vol. 76).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AU - Winkler, Till J.

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Walke F, Bennek L, Winkler TJ. AI in Government: A Study on Explainability of High-risk AI-systems in Law Enforcement and Police Service. In Beverungen D, Lehrer C, Trier M, editors, Transforming the Digitally Sustainable Enterprise: Proceedings of the 18th International Conference on Wirtschaftsinformatik, Paderborn, Germany, 2023, Vol. 3. Cham: Springer. 2025. p. 393-407. (Lecture Notes in Information Systems and Organisation, Vol. 76). doi: 10.1007/978-3-031-80125-9_23