Apple Card Credit Assessment Algorithm: Potential Bias towards Men - A Case Study in Safe and Secure AI
November 11, 2019
The Apple Card's credit assessment algorithm has raised concerns over gender bias, with men receiving significantly higher credit limits than women with comparable credit qualifications. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help prevent such incidents and promote trustworthy AI? JOIN US
- Alleged deployer
- goldman-sachs
- Alleged developer
- apple
- Alleged harmed parties
- apple-card-female-users, apple-card-female-credit-applicants
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/92
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.