AI incident ·
Uber Locked Indian Drivers out of Accounts Allegedly Due to Facial Recognition Fails
On this page 9 sections
In brief
An AI system built by Uber and Azure Cognitive Services and deployed by Uber allegedly harmed Uber drivers in India.
- Risk domain
- Discrimination and Toxicity
- Occurred
- Coverage
- 3 reports
What happened
Uber drivers in India reported being locked out of their accounts allegedly due to Real-Time ID Check's facial recognition failing to recognize appearance changes or faces in low lighting conditions.
Laws on record for this risk area
Policy angle: Classified under Discrimination and Toxicity (Unequal performance across groups) in the MIT AI Risk Repository taxonomy; 4 instruments on record cover this risk area.
- 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 category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (3)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Uber
- Alleged developer
- Uber Azure Cognitive Services
- Alleged harmed party
- Uber drivers in India
AI systems implicated
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Discrimination and Toxicity
- Risk subdomain
- 1.3 Unequal performance across groups
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 1.3.
- Bias and discrimination (value embedding)
"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models...
- Impact on affected communities
"It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the r...
- Unfair capability distribution
"Performing worse for some groups than others in a way that harms the worse-off group"
- Fairness
Avoiding bias and ensuring no disparate performance
- Disparate Performance
The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups...
- Ideological Homogenization from Value Embedding
"The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world c...
- Fairness
This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved o...
- Alienation
Alienation is the specific self-estrangement experienced at the time of technology use, typically surfaced through interaction with systems that under-perform for marginalized individuals
Incidents in the same risk subdomain
- Washington State DOL's AI Phone System Reportedly Failed to Provide Spanish-Language Service to Callers Requesting Spanish
- UK Facial Recognition System Reportedly Exhibits Higher False Positive Rates for Black and Asian Subjects
- Infinite Campus AI-Driven Student Risk Model Leads to Cuts in Support for Nevada's Low-Income Schools
- Police Use of Facial Recognition Software Causes Wrongful Arrests Without Defendant Knowledge
- Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud
- Facewatch Reported to Have Wrongfully Flagged Home Bargains Customer as Shoplifter
Other incidents involving Uber
- Uber Launched Opaque Algorithm That Changes Drivers' Payments in the US
- Uber Allegedly Violated GDPR by Failing to Provide Sufficient Notice on Automated Profiling for Drivers
- Uber Allegedly Wrongfully Accused Drivers of Fraud via Automated Systems
- Transgender Uber Drivers Mistakenly Kicked off App for Appearance Change during Gender Transitions
- Uber AV Killed Pedestrian in Arizona
- Uber's Surge Pricing Reportedly Offered Disproportionate Service Quality along Racial Lines
Source record: incident #418 on the AI Incident Database · all 3 reports