Abstract: A growing body of work in algorithmic fairness has brought issues around 'data bias' to the forefront. Often, these discussions have focused on ways in which marginalized communities are under- and mis-represented in datasets, contributing to unintended consequences in algorithmic decision-making. In this talk, we argue that these challenges are only a part of a broader set of data inequalities. We present a framework encompassing data exclusion, inclusion, accuracy, usability, access, and ownership. Using examples in criminal justice, public service provisions, and health, we demonstrate ways in which processes in data generation, access, and use create and amplify harms in algorithmic decision-making. We conclude with a discussion on ways to combat these challenges using interventions drawing from a range of disciplines.

Bio: Rediet Abebe is an Assistant Professor of Computer Science at the University of California, Berkeley and a Junior Fellow at the Harvard Society of Fellows. Abebe holds a Ph.D. in computer science from Cornell University and graduate degrees in mathematics from Harvard University and the University of Cambridge. Abebe co-founded and co-organizes Mechanism Design for Social Good (MD4SG) and is serving as Program Co-chair for the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21). Her dissertation received the 2020 ACM SIGKDD Dissertation Award and an honorable mention for the ACM SIGEcom Dissertation Award for offering the foundations of this emerging research area. Abebe's work has informed policy and practice at the National Institute of Health (NIH), the Ethiopian Ministry of Education, and numerous non-profit organizations. Abebe also co-founded Black in AI, a non-profit organization tackling equity issues in AI. Her research is influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.