AI incident ·
Apple Card's Credit Assessment Algorithm Allegedly Discriminated against Women
In brief
An AI system built by Apple and deployed by Goldman-Sachs allegedly harmed Women and girls, Women and 2 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 6 reports
What happened
Apple Card's credit assessment algorithm was reported by Goldman-Sachs customers to have shown gender bias, in which men received significantly higher credit limits than women with equal credit qualifications.
Laws that address this harm
Policy angle: Classified under Discrimination and Toxicity (Unfair discrimination and misrepresentation) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case in the United States.
- Colorado AI Act
- NYC Local Law 144 (automated employment decision tools)
- Illinois AI Video Interview Act
- Illinois HB 3773 (AI in employment, Human Rights Act amendment)
- NIST AI RMF
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (6)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Goldman-Sachs
- Alleged developer
- Apple
- Alleged harmed party
- Women and girls Women Apple card users Apple card credit applicants
AI systems implicated
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.1 Unfair discrimination and misrepresentation
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- AI tangible harm event
- Sectors
- financial and insurance activities
- Countries
- UNITED STATES
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 1.1.
- Discrimination
"Discrimination - Unfair or inadequate treatment or arbitrary distinction based on a person’s race, ethnicity, age, gender, sexual preference, religion, national origin, marital status, disability, language, or other pro...
- Harms of Representation and Other Biases
"A pretrained LLM generally has many of the stereotypical biases commonly present in the human society (Touvron et al., 2023). This makes it difficult for users to trust that LLMs will work well for them and not produce...
- Risks from bias and underrepresentation
"The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains su...
- Bias
"General-purpose AI systems can amplify social and political biases, causing concrete harm. They frequently display biases with respect to race, gender, culture, age, disability, political opinion, or other aspects of hu...
- Biased Training Data
"Compared with the definition of toxicity, the definition of bias is more subjective and contextdependent. Based on previous work [97], [101], we describe the bias as disparities that could raise demographic differences...
- Toxicity and Bias Tendencies
"Extensive data collection in LLMs brings toxic content and stereotypical bias into the training data."
- Bias
"The training datasets of LLMs may contain biased information that leads LLMs to generate outputs with social biases"
- Broken systems
"These are the most mentioned cases. They refer to situations where the algorithm or the training data lead to unreliable outputs. These systems frequently assign disproportionate weight to some variables, like race or g...
Related incidents
Linked by editors or by text similarity in the source dataset.
Incidents in the same risk subdomain
- DOGE Reportedly Relied on Unvetted ChatGPT Outputs in Canceling National Endowment for the Humanities Grants
- Sora Video Generator Has Reportedly Been Creating Biased Human Representations Across Race, Gender, and Disability
- Meta AI Characters Allegedly Exhibited Racism, Fabricated Identities, and Exploited User Trust
- Algorithmic Bias in French Welfare System Allegedly Discriminates Against Marginalized Groups
- Alleged AI-Generated Photo Alteration Leads to Inappropriate Modifications in Speaker's Conference Picture
- Department for Work and Pensions (DWP) AI Systems Allegedly Discriminate Against Single Mothers
Source record: incident #92 on the AI Incident Database · all 6 reports