Allison Koenecke

Allison Koenecke is an assistant professor of information science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. 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 previously held a postdoctoral researcher role at Microsoft Research and received her Ph.D. from Stanford’s Institute for Computational and Mathematical Engineering. She is the recipient of several NSF grants and a Cornell CIS DEIB Faculty of the Year Award, and has been honored as a Sloan Fellow in Computer Science and a Forbes 30 Under 30 lister in Science. Koenecke is regularly quoted as an expert on disparities in automated speech-to-text systems. She has been featured in prominent news outlets, including the New York Times, the Associated Press, the Atlantic, Forbes, Business Insider, Wired, and Scientific American. Her work has been published in venues including Nature, PNAS, NeurIPS, and FAccT.

AI2050 Project

Automated speech recognition (ASR) systems are used in high-stakes settings including job interviews, courtrooms, and hospitals, but no independent benchmarking infrastructure exists to hold them accountable. Koenecke’s project collects multi-domain, multi-accent speech datasets to benchmark ASR services, with the goal of informing procurement decisions in these contexts. The resulting benchmarks — published as an open leaderboard — will disaggregate performance across speaker types (such as African American English speakers, ESL learners, or people with speech impairments), supporting downstream analysis of audio factors underlying poor ASR performance and guiding concrete improvements in training data collection, data processing, and model architecture.