Computer Science Colloquium, Allison Koenecke, Microsoft Research

Friday, October 22

2:35pm in Wege (TCL 123)

Racial Disparities in Automated Speech Recognition

Automated speech recognition (ASR) systems are now used in a variety of applications to convert spoken language to text, from virtual assistants, to closed captioning, to hands-free computing. By analyzing a large corpus of sociolinguistic interviews with white and African American speakers, we demonstrate large racial disparities in the performance of popular commercial ASR systems developed by Amazon, Apple, Google, IBM, and Microsoft. Our results point to hurdles faced by African Americans in using increasingly widespread tools driven by speech recognition technology. More generally, our work illustrates the need to audit emerging machine-learning systems to ensure they are broadly inclusive. See more at fairspeech.stanford.edu.

Allison Koenecke is a postdoc at Microsoft Research in the Machine Learning and Statistics group, and starting Summer 2022 will be an Assistant Professor of Information Science at Cornell University.  Her research primarily spans two domains: algorithmic fairness in online services, and causal inference in public health.  Previously, she received her PhD from Stanford’s Institute for Computational & Mathematical Engineering, and her Bachelor’s from MIT in Mathematics with Computer Science.