Apple Card's Credit Assessment Algorithm Allegedly Discriminated against Women

November 11, 2019

Goldman-Sachs customers reported gender bias in the credit assessment algorithm of Apple Card, with men receiving higher credit limits than women with equal qualifications. This incident underscores the importance of trustworthy AI and harm prevention, emphasizing the need for safe and secure AI practices.

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Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

GOVERN 3.1 — similarity 0.684, rank 1. TAIM detail and related incidents →
MEASURE 2.6 — similarity 0.682, rank 2. TAIM detail and related incidents →
MAP 4.1 — similarity 0.680, rank 3. TAIM detail and related incidents →

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

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