ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1602998
This article is part of the Research TopicContesting Artificial Intelligence: Communicative Practices, Organizational Structures, and Enabling TechnologiesView all articles
Contestable AI for Criminal Intelligence Analysis: Improving Decision-Making Through Semantic Modeling and Human Oversight
Provisionally accepted
Falk Maoro*
Michaela Geierhos- Universität der Bundeswehr München, Neubiberg, Germany
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Criminal investigation analysis involves processing large amounts of data, making manual analysis impractical. Artificial intelligence (AI)-driven information extraction systems can assist investigators in handling this data, leading to significant improvements in effectiveness and efficiency. However, the use of AI in criminal investigations also poses significant risks to individuals, requiring the integration of contestability into systems and processes. To meet this challenge, contestability requirements must be tailored to specific contexts. In this work, we analyzed and adapted existing requirements for criminal investigation analysis, focusing on the retrospective analysis of police reports. For this purpose, we introduced a novel information extraction pipeline based on three language modeling tasks, which we refer to as semantic modeling. Building on this concept, we evaluated contestability requirements and integrated them into our system. As a proof of concept, we developed an AI-driven information extraction system that incorporates contestability features and provides multiple functionalities for data analysis. Our findings highlight three key perspectives essential for contestability in AI-driven investigations: information provision, interactive controls, and quality assurance. This work contributes to the development of more transparent, accountable, and adaptable AI systems for law enforcement applications.
Keywords: contestability requirements, AI-driven information extraction, named entity recognition, semantic triples, sequence classification, Relationship extraction
Received: 30 Mar 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Maoro and Geierhos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Falk Maoro, Universität der Bundeswehr München, Neubiberg, Germany
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