Fairness, Accountability, and Transparency in Predictive Models for Criminal Justice

Explore a comprehensive lecture on fairness, accountability, and transparency in predictive modeling within criminal justice systems. Delve into Kristian Lum's research from the University of Pennsylvania, examining crucial examples that highlight these concepts' significance. Investigate the representativeness of police records in crime data, analyze drug crimes in Oakland through simulations and demonstrations, and understand the importance of fairness in law enforcement practices. Examine pre-trial risk assessments, including the Public Safety Assessment, and evaluate empirical risk estimates within decision-making frameworks. Discover the implications of risk assessment in supervised release programs and the benefits of hand selection. Gain insights into the validation process of predictive models and their impact on different racial groups. Conclude with a final summary emphasizing the critical nature of transparency in criminal justice applications of predictive modeling.