AI incident ·
Amazon's Experimental Hiring Tool Allegedly Displayed Gender Bias in Candidate Rankings
In brief
An AI system built and deployed by Amazon allegedly harmed Women applying to Amazon, Women and girls and 2 others.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 34 reports
What happened
Between 2014 and 2017, Amazon reportedly developed an AI-powered recruiting tool to score job applicants, trained on a decade of resumes purportedly drawn largely from men. Media reports say the system learned to favor male candidates, penalizing terms like "women's" and graduates from certain all-women's colleges. Efforts to remove these biases reportedly did not guarantee fairness, and the project was ultimately abandoned. Amazon reportedly states recruiters never solely relied on the tool.
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 Ireland.
- Colorado AI Act
- India DPDP Act
- Law No. 132/2025 on artificial intelligence
- NYC Local Law 144 (automated employment decision tools)
- EU AI Act
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 (34)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Amazon
- Alleged developer
- Amazon
- Alleged harmed party
- Women applying to Amazon Women and girls Women Amazon applicants
AI systems implicated
Enterprise AI systemsAssociated machine learning models trained on historical Amazon resume dataAmazon experimental AI resume scoring engineAI-enabled decision support systems
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 issue
- Sectors
- administrative and support service activities
- Countries
- IE
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
Other incidents involving Amazon
- Amazon Algorithmic Pricing Allegedly Hiked up Price of Reference Book to Millions
- Amazon Reportedly Sold Products and Recommended Frequently Bought Together Items That Aid Suicide Attempts
- Alexa Recommended Dangerous TikTok Challenge to Ten-Year-Old Girl
- Amazon's AI Cameras Incorrectly Penalized Delivery Drivers for Mistakes They Did Not Make
- Amazon's Monitoring System Allegedly Pushed Delivery Drivers to Prioritize Speed over Safety, Leading to Crash
- Amazon Allegedly Forced Deployment of AI-Powered Cameras on Delivery Drivers
Source record: incident #37 on the AI Incident Database · all 34 reports