This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
Uber drivers in the UK and EU filed legal actions against the company for failing to provide access to personal data and transparency regarding automated decision-making and profiling systems used in the Uber Driver App. The litigation revealed that Uber used opaque algorithms to manage drivers, including performance tagging and automated account deactivations based on fraud allegations. A Dutch appeals court eventually ruled that Uber violated drivers' rights by failing to provide sufficient transparency and meaningful human review for these automated decisions.
Uber was alleged in a lawsuit to have provided incomplete notice about automated decision-making and profiling for drivers such as information about their driving behavior, and use of phone.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Delegating by humans of key decisions to AI systems, or AI systems that make decisions that diminish human control and autonomy, potentially leading to humans feeling disempowered, losing the ability to shape a fulfilling life trajectory, or becoming cognitively enfeebled.
Also classified as
The use of automated 'robo-firings' and opaque dynamic pricing structures degraded working conditions and increased the power imbalance between the platform and workers.
Uber delegated key employment decisions, including account terminations, to automated systems with no meaningful human review or appeal route, removing human agency.
AI system
Due to a decision or action made by an AI system
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The incident was caused by Uber's intentional deployment of automated decision-making and profiling algorithms to manage and terminate drivers post-deployment.
Added categories peak at 2: Minor (Psychological Harm, Direct)
A legal dispute over Uber's use of opaque algorithms for driver profiling and automated terminations in the UK and EU. While highlighting significant concerns regarding AI-driven labor management and GDPR compliance, the national security implications remain negligible to minor, with standard judicial mechanisms successfully addressing the issue.
The Uber Driver App operates autonomously to make real-time decisions regarding ride allocation, pricing, and account deactivations based on driver data and profiling, often with no human in the loop.
Risk Level 2: High Risk. The system is an employment and worker management system used for monitoring, evaluating, and terminating workers, which is classified as high-risk under the EU AI Act.
The system is designed to monitor driver performance, track metrics, and evaluate fraud probability, while also administering work allocation and account deactivations.
The reports provide highly detailed legal and journalistic accounts of court filings, appeals court rulings, and statements from both the litigants and Uber. The facts regarding GDPR violations and automated deactivations are well-documented.
Uber argued that deactivations involved human review and were not purely automated, though the court found this human review to be merely symbolic.
Technology
Opaque Algorithmic Management
Algorithms control dispatch, pricing, and terminations without transparency.
Automated Robo-Firing Systems
Algorithms automatically deactivate accounts based on suspected fraud.
Dynamic Pricing Opacity
Personalized and dynamic pricing algorithms mask how pay is calculated.
Data Inputs
Secret Performance Profiles
Drivers are profiled using hidden tags like attitude and cancellations.
Incomplete Data Disclosure
Uber provides limited datasets, withholding fraud scores and ratings.
Asymmetric Data Collection
Platforms harvest vast driver data while denying workers access to it.
Human Factors
Lack of Meaningful Human Review
Staff perform symbolic checks rather than actual reviews of deactivations.
Remote Decision Making
Remote staff in Krakow dismiss drivers without giving them a hearing.
Process and Methods
Stonewalling and Obfuscation
Uber uses trade secrets defense to deny drivers access to algorithmic logic.
Unjust Automated HR Processes
Platforms build automated HR systems with no humans in the loop.
Management
Information Power Asymmetry
Management exploits data control to suppress collective bargaining.
Cost-Cutting Automation
Management automates HR to save money and avoid employer responsibilities.
Abuse of Trade Secrets
Management uses proprietary claims to hide algorithmic control.
Regulatory Environment
Weak Enforcement of GDPR
Regulators fail to ensure swift platform compliance with data requests.
Proposed Deregulation
UK draft bill threatens to strip workers of existing data protections.
Inactive Oversight Bodies
Regulators like TfL fail to review changes to upfront pricing models.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
