Allison Koenecke is a PhD candidate at Stanford's Institute for Computational & Mathematical Engineering.  Her research interests lie broadly at the intersection of economics and computer science, and her projects focus on algorithmic fairness in online applications and causal inference in public health.  She previously specialized in antitrust at NERA Economic Consulting after graduating from MIT with a Bachelor's in Mathematics with Computer Science.

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 demonstrate large racial disparities in the performance of popular commercial speech-to-text systems developed by Amazon, Apple, Google, IBM, and Microsoft, a pattern likely stemming from a lack of diversity in the data used to train the systems. These results point to hurdles faced by African Americans 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.  In particular, I discuss how to formulate fair decisions considering budget-constrained trade-offs between English-speaking and Spanish-speaking SNAP applicants.  Both projects exemplify processes to ameliorate demographic-based disparate impact arising from decisions made by online platforms.