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Uber Allegedly Wrongfully Accused Drivers of Fraud via Automated Systems

5 news reports Synced from source · record last edited 5 Sep 2026

In brief

An AI system built and deployed by Uber allegedly harmed Uber drivers.

Risk domain
AI system safety, failures, and limitations Lack of transparency or interpretability
Occurred
Coverage
5 reportsJul 2020 - Apr 2023

What happened

Uber was alleged in a lawsuit to have wrongfully accused its drivers in the UK and Portugal of fraudulent activity through automated systems, which resulted in their dismissal without a right to appeal.

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.

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.

  1. UK Uber drivers are taking the algorithm to court
    techcrunch.com · Natasha Lomas
  2. App Drivers & Couriers Union files ground-breaking legal challenge against Uber’s dismissal of drivers by algorithm in the UK and Portugal
    adcu.org.uk · App Drivers and Couriers Union
  3. Dutch court rulings break new ground on gig worker data rights
    ft.com · Delphine Strauss, Siddharth Venkataramakrishnan
  4. Historic digital rights win for WIE and the ADCU over Uber and Ola at Amsterdam Court of Appeal
    workerinfoexchange.org · Worker Info Exchange

Who was involved

Alleged deployer
Uber
Alleged developer
Uber
Alleged harmed party
Uber drivers

AI systems implicated

Algorithmic management systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
AI tangible harm event
Sectors
transportation and storage
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)."

    AGI Safety Literature Review (Everitt2018 )

  • 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...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

  • 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...

    Future Risks of Frontier AI (GOS2023)

  • 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...

    What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review (Giarmoleo2024)

  • 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...

    Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  • Model Evaluations (Interpretability/Explainability)

    Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  • 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...

    Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

  • 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...

    Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Uber

Source record: incident #355 on the AI Incident Database · all 5 reports