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Contestable AI for criminal intelligence analysis: improving decision-making through semantic modeling and human
Falk Maoro1, Michaela Geierhos1
1Research Institute CODE, University of the Bundeswehr Munich, Neubiberg, Germany.
Frontiers in Artificial Intelligence
|July 16, 2025
Summary
Artificial intelligence (AI) aids criminal investigations by processing data, but requires contestability. This study integrates contestability into AI systems for transparent and accountable law enforcement analysis.
Area of Science:
- Computer Science
- Law Enforcement Technology
Background:
- Manual criminal investigation analysis is impractical due to large data volumes.
- Artificial intelligence (AI) offers efficiency but introduces risks requiring contestability.
Purpose of the Study:
- To adapt and integrate contestability requirements into AI systems for criminal investigation analysis.
- To develop an AI-driven information extraction system with built-in contestability features.
Main Methods:
- Developed a novel information extraction pipeline using three language modeling tasks (semantic modeling).
- Analyzed and adapted existing contestability requirements for retrospective police report analysis.
- Integrated contestability features into a proof-of-concept AI system.
Main Results:
- Identified three key perspectives for contestability: information provision, interactive controls, and quality assurance.
- Demonstrated an AI system incorporating these contestability features for data analysis.
- Showcased the feasibility of tailored contestability in AI for criminal investigations.
Conclusions:
- AI systems for criminal investigations must prioritize transparency, accountability, and adaptability.
- Contestability is crucial for mitigating risks associated with AI in law enforcement.
- This work provides a framework for developing more trustworthy AI in criminal justice.
Keywords:
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