This use case applies AI4Gov tools to detect bias and inconsistencies in breathalyser testing and traffic accident data in Slovenia. By analysing regional variations and data completeness, it supports fairer and more transparent road-safety policies aligned with the Sustainable Development Goals (SDGs).
Key points:
- Detection of inconsistencies and data bias in alcohol-related traffic incidents across administrative regions
- Fairness assessment of law enforcement practices related to alcohol checks and traffic accident reporting
- Identification of underreporting and selective enforcement patterns affecting data reliability
- Comparison of accident rates with breathalyser testing frequency to uncover regional disparities
- AI-driven visualisation of traffic safety data, enabling evidence-based policy interventions
- Support for unbiased, data-informed policymaking to promote fairer traffic enforcement and safer roads
- Replicable framework for bias detection applicable to other public safety and policy domains
