AI incident ·
HUD charges Facebook with enabling housing discrimination
In brief
An AI system built and deployed by Facebook allegedly harmed Non American Born Facebook Users, Non Christian Facebook Users and 3 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 4 reports
What happened
In March 2019 the U.S. Department of Housing and Urban Development charged Facebook with violating the Fair Housing Act by allowing real estate sellers to target advertisements in a discriminatory manner.
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 (4)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
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
- none
- Sectors
- information and communication
- Countries
- US
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...
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
Other incidents involving Facebook
- Facebook's Automated Moderation Allowed Ads Threatening Election Workers to be Posted
- Facebook AI-Supported Moderation for Ads Failed to Detect Violating Content
- Facebook’s Hate Speech Detection Algorithms Allegedly Disproportionately Failed to Remove Racist Content towards Minority Groups
- Facebook Internally Reported Failure of Ranking Algorithm, Exposing Harmful Content to Viewers over Months
- Facebook's Automated Moderation Flagged Gardening Group's Language Use by Mistake
- Facebook's Automated Tools Failed to Adequately Remove Hate Speech, Violence, and Incitement
Source record: incident #93 on the AI Incident Database · all 4 reports