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Allison Koenecke
Allison Koenecke
Bio: Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech. 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 and a Cornell CIS DEIB Faculty of the Year Award, and has been honored as a Sloan Fellow in Computer Science and Forbes 30 Under 30 lister in Science.
Abstract: Automated speech recognition (ASR) systems are used in a variety of applications to convert spoken language to text -- from scribing patient notes, to conducting hiring interviews, to writing police narrative reports. The risks of ASR underperformance are real, and disproportionately placed on certain demographics of speakers – often those who are marginalized in the contexts in which ASR is being used. We argue for more principled audits to be conducted on ASR systems, analogous to post-marketing surveillance for medical devices. Our framework for doing so involves: (1) collecting diverse, domain-specific speech datasets representative of real patient and provider populations, (2) developing metric suites that go beyond the singular gold standard of Word Error Rates, and (3) conducting human-centered design research to align functionality with user needs. We conclude by discussing possibilities for the “who, what, when, and how” of functionally conducting such audits.
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