Seminar Announcement: Racial Disparities in Automated Speech Recognition

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RACIAL DISPARITIES IN AUTOMATED SPEECH RECOGNITION

Summary
From virtual assistants to closed captioning, to hands-free computing. Join RI-AI Meetup and Allison Koenecke of Stanford’s Institute for Computational and Mathematical Engineering as she examines the inclusivity of Automated Speech Recognition (ASR) systems used to convert spoken language to text.

Abstract
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.

Speaker Bio
Allison Koenecke is a Ph.D. Candidate of Stanford’s Institute for Computational and Mathematical Engineering. She researches the intersection of economics and computer science, with projects focusing on fairness in algorithmic systems and causal inference in the public health space.