Title: Fairness in Algorithmic Services
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. In the former, I audit popular speech-to-text systems (developed by Amazon, Apple, Google, IBM, Microsoft, and OpenAI) and demonstrate disparities in transcription performance for African American English speakers, and speakers with language impairments (patterns likely stemming from a lack of diversity in the data used to train the systems). These results point to hurdles faced by non-“Standard” English speakers in using widespread tools driven by speech recognition technology. In the second part of the talk, I propose a methodological framework for online advertisers to determine a demographically equitable allocation of individuals being shown ads for SNAP (food stamp) benefits. This framework measures what different populations believe is a “fair” allocation of ad budgets in a constrained setting, given cost trade-offs between English-speaking and Spanish-speaking SNAP applicants; I uncover broad consensus across demographics for some degree of equity over pure efficiency. Both projects exemplify processes to reduce disparate impact in algorithmic decision-making.
Bio: Allison Koenecke is an Assistant Professor of Information Science at Cornell University. Her research on algorithmic fairness 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. She is the recipient of several NSF grants, the Forbes 30 Under 30 in Science, and the Cornell CIS DEIB Faculty of the Year Award.
