The multifaceted relationship between crime, policing, and society has long been a subject of controversy, particularly with the advent of artificial intelligence (AI) algorithms in crime prediction and predictive policing. In this intriguing episode, we have the pleasure of hosting Dr. Ishanu Chattopadhyay, whose work demonstrates how predictive models can do more than just enhance state power—they can offer an unprecedented insight into systemic biases in crime enforcement.

Dr. Chattopadhyay and his team introduce a novel stochastic inference algorithm that learns spatio-temporal dependencies from crime reports, allowing for impressive accuracy in crime forecasting. Yet, this breakthrough in prediction has far-reaching implications beyond law enforcement efficacy. It offers a powerful tool for social analysis, uncovering evidence of bias in the response to crime based on neighborhood socio-economic status. This can divert policy resources away from disadvantaged areas, perpetuating social inequality—an alarming trend observed across eight major US cities.

Join us as we delve into Dr. Chattopadhyay's transformative work, which stands at the intersection of advanced predictive technology and social justice. Discover how AI can be used not only as a surveillance tool for crime but also as a mirror reflecting societal biases in crime enforcement.

Keywords: Predictive Policing, Crime, Artificial Intelligence, Systemic Bias, Socio-economic Status, Surveillance, Dr. Ishanu Chattopadhyay.

Rotaru, V., Huang, Y., Li, T. et al. Event-level prediction of urban crime reveals a signature of enforcement bias in US cities. Nat Hum Behav (2022). https://doi.org/10.1038/s41562-022-01372-0