Crime Story

Research on "crime story" analysis spans diverse applications, from predicting crime hotspots using spatiotemporal data and topic modeling to detecting biases in AI systems that process crime-related information. Current efforts focus on improving the accuracy and fairness of AI models for crime prediction and analysis, employing techniques like transformer networks, XGBoost, and methods to mitigate biases stemming from dialect prejudice or socioeconomic factors. These advancements aim to enhance both the efficiency of crime prevention strategies and the equitable application of justice, while also raising critical ethical considerations regarding algorithmic bias and fairness in AI-driven decision-making.