PhD Candidate
Stanford University
Areas of Interest
- Computational Social Science
- Algorithmic Fairness
- Causal Inference
- Public Health
- Artificial Intelligence
Poster
Racial Disparities in Automated Speech Recognition
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.
Bio
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 fairness in machine learning and causal inference in the public health space. She previously specialized in antitrust at NERA Economic Consulting after graduating from MIT with a Bachelor's in Mathematics with Computer Science.
