Examining Amazon's Biased Recruiting Algorithm: A Case for Safe and Secure AI
This AI incident sheds light on the potential dangers of unchecked algorithms, particularly in the context of hiring practices. The case of Amazon's gender-biased algorithm underscores the importance of trustworthy AI and responsible AI governance. It maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help prevent such incidents? JOIN US
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/37
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.