VIKING investigated how artificial intelligence methods can be used responsibly in police investigations. Complex investigations, such as those involving organized crime or terrorism, can generate large amounts of digital data. AI-based methods can assist investigators in their analysis, but they must be reliable, traceable, and legally verifiable for admission in court.

A key focus was therefore on identifying and reducing algorithmic biases, as well as transparently communicating the uncertainties inherent in model-based results. VIKING pursued an interdisciplinary approach that combined technical methods with legal and ethical requirements, as well as contributions to the standardization and regulation of testing and evaluation procedures.

The University of Konstanz served as the technical and scientific coordinator for VIKING, developing a holistic framework for trustworthy artificial intelligence for police applications and advising other work packages in using XAI techniques.

Research Questions

• How can AI-based analysis methods be designed so that their results remain transparent and understandable?
• How can the accuracy and robustness of AI systems be measured and improved systematically?
• How can biases in data and models be identified and reduced?
• Which legal and ethical requirements must be considered when developing and using AI methods for police applications?
• How can verifiable standards and testing procedures for trustworthy AI be developed?

Selected Results

For the first time, VIKING provided a set of requirements containing concrete, actionable, ethical, and legal guidelines for the development and use of AI methods in police applications. Furthermore, the results from VIKING can advance the use of AI methods by law enforcement in the future and thus contribute to strengthening the rule of law and security in Europe.

The results include:

• a comprehensive framework for trustworthy AI in police applications (DIN SPEC 91517:2025-05),
• methods for examining transparency, explainability and fairness,
• contributions to test catalogues and evaluation procedures,
• development of trustworthy AI methods in police applications,
• scientific publications on Explainable AI, time-series analysis, Visual Analytics and the interactive analysis of complex models.