CS&E Colloquium: Devansh Saxena
The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. More details about the spring 2023 series will be provided at the beginning of the semester. This week's speaker, Devansh Saxena (Marquette University), will be giving a talk titled "Designing Human-Centered Algorithms for the Public Sector: A Case Study of the Child-Welfare System".
Public sector agencies in the United States are increasingly seeking to emulate business models of the private sector centered in efficiency, cost reduction, and innovation through the adoption of algorithmic systems. These data-driven systems purportedly improve decision-making; however, the public sector poses its own unique challenges where all decisions are mediated by policies, practices, and organizational constraints. Drawing upon a case study of the child-welfare system, I highlight how algorithms that do not account for these pertinent aspects of professional practice frustrate caseworkers and diminish the quality of human discretionary work. Why haven’t these algorithms lived up to expectations? And how might we be able to improve them? A human-centered research agenda can help us develop algorithms centered in social-ecological theories that support the decision-making processes of caseworkers, incorporate novel sources of data, as well as offer a means to evaluate algorithms in their real-world contexts.
Devansh Saxena is a doctoral candidate in the Department of Computer Science at Marquette University and a member of the Social and Ethical Computing Research Lab where he is co-advised by Dr. Shion Guha and Dr. Michael Zimmer. His research interests include investigating and developing algorithmic systems employed in the public sector, especially the Child-Welfare System. His current research examines collaborative child-welfare practices where decisions are mediated by policies, practices, and algorithms. His work is driven by Human-Centered Data Science and sits at the intersection of Human-Computer Interaction, Machine Learning, and FAccT (Fairness, Accountability, and Transparency in Sociotechnical Systems).