Understanding the Context and Consequences of Pre-trial Detention

Explore the complex issues surrounding pre-trial detention and risk assessment tools in the criminal justice system through this 52-minute conference talk from FAT* 2018. Gain insights from experts Elizabeth Bender, Kristian Lum, and Terrence Wilkerson as they delve into the context and consequences of predictive modeling in criminal justice. Examine the ProPublica article "Machine Bias" and its impact on discussions of racial bias in predictive software. Analyze various notions of fairness and computational methods proposed to address these issues. Learn about the legal processes behind pre-trial detention decisions and hear firsthand experiences from someone who has been through the system. Develop a deeper understanding of the human impact of these decisions beyond just data points. Designed for quantitative researchers working with criminal risk assessment datasets, this talk provides crucial context for interpreting and applying fairness metrics in real-world scenarios.

FAT* 2018 Translation Tutorial: Understanding the Context and Consequences of Pre-trial Detention