Auditing Automated Speech Recognition Systems for Disparities
Presented by Dr. Allison Koenecke
Dr. Allison Koenecke’s research on algorithmic fairness leverages computational methods (e.g., ML, causal inference) to examine societal inequities across domains. She has received three NSF grants, was named to Forbes 30 Under 30 in Science, and earned Cornell’s CIS DEIB Faculty of the Year award.
Abstract
Automated speech recognition (ASR) systems convert spoken language to text across applications, but Allison’s audit of commercial ASR systems (e.g., OpenAI, Amazon, Apple, Google, IBM, Microsoft) reveals notable underperformance for African American English speakers and individuals with language disorders like aphasia. Her findings quantify these disparities and identify the specific, underlying ML-driven causes. Allison underscore the importance of regular audits to ensure emerging speech systems serve all users inclusively.