Assessing Hiring Algorithms for Bias: An Examination of AI Governance
This article discusses the challenge of auditing hiring algorithms for bias, emphasizing the importance of AI governance for ensuring fairness in decision-making processes. The insights provided here map to the Govern function in the HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to contribute towards responsible AI and safe and secure AI? 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.