Gender Bias in AI Recruitment Tool: Amazon's Experimental Hiring System Allegedly Favored Male Candidates
August 10, 2016
This AI incident involving Amazon's experimental recruiting tool highlights the importance of trustworthy and unbiased AI. The system, trained on a decade-old dataset predominantly from male applicants, reportedly developed a gender bias, penalizing resumes with terms like 'women's' and graduates from all-women's colleges. Despite attempts to remove biases, the tool favored male candidates. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help prevent such incidents and promote safe and secure AI? JOIN US
- Alleged deployer
- amazon
- Alleged developer
- amazon
- Alleged harmed parties
- amazon-applicants, women-applying-to-amazon
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.