AI incident ·
Uber Launched Opaque Algorithm That Changes Drivers' Payments in the US
In brief
An AI system built and deployed by Uber allegedly harmed Uber drivers.
- Risk domain
- AI system safety, failures, and limitations
- Occurred
- Coverage
- 3 reports
What happened
Uber launched a new but opaque algorithm to determine drivers' pay in the US which allegedly caused drivers to experience lower fares, confusing fare drops, and a decrease in rides.
Laws that address this harm
Policy angle: Classified under AI system safety, failures, and limitations (Lack of transparency or interpretability) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
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 (3)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
AI systems implicated
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.4 Lack of transparency or interpretability
- Causal entity
- AI
- Intent
- Intentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 7.4.
- Intelligibility
"How can we build agent’s whose decisions we can understand? Con- nects explainable decisions (Berkeley) and informed oversight (MIRI)."
- Opacity (the black box problem)
"Opacity surrounding the technical, internal decision-making processes of generative AI models is popularly known as the “black box problem.”277 Generative AI models, most ubiquitously built on deep neural networks with...
- Lack of transparency and interpretability
"Today's Frontier AI is difficult to interpret and lacks transparency. Contextual understanding of the training data is not explicitly embedded within these models. They can fail to capture perspectives of underrepresent...
- Attributing the responsibility for AI's failures
"This section, constituting almost 8% of the articles, addresses the implications arising from AI acting and learning without direct human supervision, encompassing two main issues: a responsibility gap and AI's moral st...
- General Evaluations (Difficulty of identification and measurement of capabilities)
"The capabilities of general-purpose AI systems can be difficult to measure, compared to the capabilities of more limited and fixed-purpose AI systems. This is in part due to a broader distribution of potential risks, a...
- Model Evaluations (Interpretability/Explainability)
- Model outputs inconsistent with chain-of-thought reasoning
"Chain-of-thought reasoning is sometimes employed to get a better understanding of the model’s output, where it encourages transparent reasoning in text form. However, in some cases, this reasoning is not consistent with...
- Lack of understanding of in-context learning in language models
"In-context learning allows the model to learn a new task or improve its perfor- mance by providing examples in the prompt, without changing its weights [101]. Even though this technique is highly effective, its working...
Incidents in the same risk subdomain
- Israeli Tax Authority Reportedly Used an Opaque Automated System to Issue a Fine, Declining to Explain or Disclose the Underlying Calculation
- Uber Allegedly Wrongfully Accused Drivers of Fraud via Automated Systems
- Houston ISD's EVAAS Teacher-Evaluation System Reportedly Put Teachers' Jobs at Risk Through Unverifiable Scores
- Dutch City Court Defended Home Value Generated by Black-Box Algorithm
Other incidents involving Uber
- 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 Locked Indian Drivers out of Accounts Allegedly Due to Facial Recognition Fails
- Uber's Surge Pricing Reportedly Offered Disproportionate Service Quality along Racial Lines
Source record: incident #203 on the AI Incident Database · all 3 reports