Fairness in Algorithmic Services

Fairness in Algorithmic Services (via Zoom)

Abstract: Algorithmically guided decisions are becoming increasingly prevalent and, if left unchecked, can amplify pre-existing societal biases. In this talk, I use modern computational tools to examine the equity of decision-making in two complex systems: automated speech recognition and online advertising. Furthermore, I will propose processes to ameliorate demographic-based disparate impact arising from decisions made by online platforms. First, I demonstrate large racial disparities in the performance of popular commercial speech-to-text systems developed by big tech companies, a pattern likely stemming from a lack of diversity in the data used to train the systems. Second, I present a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits.  In particular, I discuss how to formulate fair decisions considering budget-constrained trade-offs between English-speaking and Spanish-speaking SNAP applicants, and present survey results revealing greater-than-expected consensus on fairness preferences in budget allocation.

Bio: Allison Koenecke is an Assistant Professor of Information Science at Cornell University. Her research applies computational methods, such as machine learning and causal inference, to study societal inequities in domains from online services to public health. Koenecke is regularly quoted as an expert on disparities in automated speech-to-text systems. She previously held a postdoctoral researcher role at Microsoft Research and received her PhD from Stanford's Institute for Computational and Mathematical Engineering.