Description: A lawsuit by a former Uber Eats delivery driver alleged the company to have wrongfully dismissed him due to frequent false mismatches of his verification selfies, and discriminated against him via excessive verification checks.
Entities
View all entitiesAlleged: Uber Eats developed and deployed an AI system, which harmed Pa Edrissa Manjang and Uber Eats Black delivery drivers.
Risk Subdomain
A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
1.1. Unfair discrimination and misrepresentation
Risk Domain
The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
- Discrimination and Toxicity
Entity
Which, if any, entity is presented as the main cause of the risk
AI
Timing
The stage in the AI lifecycle at which the risk is presented as occurring
Post-deployment
Intent
Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
Unintentional
Incident Reports
Reports Timeline
A delivery driver who is suing Uber Eats in London over his dismissal from the company and claims its facial recognition technology is racially biased says the company treats couriers as “numbers rather than humans”.
Pa Edrissa Manjang work…
A former Uber Eats courier has brought legal action against the food delivery company, alleging he was unfairly dismissed because of the company’s “racist” facial recognition software.
Uber Eats drivers are required to take a selfie before …
Variants
A "variant" is an AI incident similar to a known case—it has the same causes, harms, and AI system. Instead of listing it separately, we group it under the first reported incident. Unlike other incidents, variants do not need to have been reported outside the AIID. Learn more from the research paper.
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