Addressing Racial Bias in Automated Speech Recognition: Steps Toward Responsible AI

Recent studies have highlighted a concerning trend of racial disparities in automated speech recognition systems, raising questions about their trustworthiness. This article discusses the findings and emphasizes the need for responsible AI governance and harm prevention measures to ensure safe and secure AI use.

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MAP 1.2 — similarity 0.558, rank 1. TAIM detail and related incidents →
GOVERN 3.1 — similarity 0.547, rank 2. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.532, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/102

Data source

Incident data is from the AI Incident Database (AIID).

When citing the database as a whole, please use:

McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.

Pre-print on arXiv · Database snapshots & citation guide

We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.