Deconstructing Risk in Predictive Risk Models

Predictive Risk Models (PRM) have become commonplace in many government agencies to provide optimal data-driven decision-making outcomes in high-risk contexts such as criminal justice, child welfare, homelessness, immigration etc. While such technology continues to be acquired and implemented rapidly throughout the government because of the perceived benefits of cost reductions and better decision-making outcomes, recent research has pointed out several issues in how PRMs are developed. Notably, existing risk assessment approaches underlie much of the training data for these PRMs. But what exactly are these PRMs predicting? In this talk, I use empirical studies in the context of child welfare to deconstruct and interrogate what "risk" in PRMs actually means and provide provocative directions for the community to discuss how we can move beyond our existing PRM development approaches.

Speaker Biography:

Shion Guha is an Assistant Professor in the Faculty of Information and cross-appointed to the Department of Computer Science at the University of Toronto. His research interests include human-computer interaction, data science, and public policy. He's been involved in developing the field of Human-Centred Data Science. This intersectional research area combines technical methodologies with interpretive inquiry to address biases and structural inequalities in socio-technical systems. He is the author of Human-Centered Data Science: An Introduction, an Amazon Best Selling textbook published by MIT Press in 2022.