Stichting WIE International v. Uber B.V. and Uber Technologies, Inc.

The lawsuit challenges Uber's use of automated decision-making and algorithmic management to dispatch rides and calculate driver compensation. Stichting WIE International contends that Uber's dynamic pricing and dispatch systems rely on opaque profiling and self-learning machine learning algorithms trained unlawfully on driver personal data. The action tests whether gig-economy platform operators can deploy dynamic, automated pay and assignment models without violating European data protection rules and collective labor rights.

Who is the plaintiff?

The plaintiff is Stichting WIE International, an independent non-profit foundation initiating collective redress on behalf of Uber drivers across Europe. The class encompasses drivers who completed at least one trip via the Uber driver app since December 1, 2020, in the Netherlands, the United Kingdom, Belgium, France, Germany, Poland, or Romania. The initiative is overseen in collaboration with advocacy group Worker Info Exchange.

What is being alleged?

The plaintiff alleges Uber violates Article 22 GDPR by subjecting drivers to solely automated decisions regarding ride allocation and pay rates without meaningful human intervention. The lawsuit further alleges Uber processes driver personal data at scale to train machine learning algorithms without a lawful legal basis under Article 6 GDPR, breaches transparency duties under Articles 12–14 GDPR, and unlawfully transferred personal data to its US parent company between August 2021 and November 2023 without appropriate safeguards.

Why is the defendant being sued?

Uber B.V. and Uber Technologies, Inc. are sued as joint controllers to force the cessation of automated dispatch and dynamic pay systems, compel the destruction of driver data used to train dynamic models, and recover damages. The claims cite economic analysis from Oxera alleging substantial drops in driver hourly earnings, alongside compensation claims for non-material distress from permanent algorithmic surveillance and unjust enrichment under the Dutch Civil Code.

For Employers and HR Teams

The case highlights substantial regulatory and collective litigation exposure for organizations using automated systems or AI to assign tasks, set variable compensation, or evaluate worker productivity. Employers operating in European jurisdictions must evaluate whether algorithmic workforce management triggers Article 22 GDPR restrictions on automated decision-making. Organizations should establish clear lawful bases before repurposing employee or contractor performance data to train internal machine learning models. Opaque algorithmic wage setting and pervasive digital monitoring also increase legal risks concerning non-material harm, unjust enrichment, and mandatory data deletion orders.

For HR Technology Vendors

For HR technology providers, this dispute underscores growing scrutiny over algorithmic profiling, dynamic compensation models, and automated dispatch logic. Providers must design systems that support meaningful human oversight rather than nominal human-in-the-loop controls, ensuring decisions affecting worker livelihood remain explainable and auditable. Software architectures must also accommodate strict data segregation, enabling customers to verify lawful processing grounds for model training and execute data destruction or opt-out mandates if algorithmic inputs are challenged. Vendors risk customer indemnification disputes if tools cause automated wage suppression or trigger collective regulatory actions.

• Provider: Uber B.V. and Uber Technologies, Inc.
• Products or Systems Named: Uber Driver App; Automated Driver Compensation System (Chauffeursvergoeding); Automated Ride Allocation System (Rittoewijzing); Dynamic Models / Machine Learning algorithms
• Employment Use Cases: Automated task allocation, dynamic driver pay determination, worker performance profiling, and algorithmic trip acceptance prediction
• Role in Decision-Making: Solely automated decision-making operating without meaningful human intervention to calculate individual trip pay rates and assign passenger rides to drivers
• Alleged Harm: Reduced hourly earnings (calculated by Oxera at an average annual drop of €7,508 in the Netherlands and £5,337 in the UK), longer working hours, unpredictable income, stress, loss of data control, and pervasive workplace surveillance
• Data or Proxy Variables Discussed: Driver personal data, historical ride records, behavioral data, acceptance likelihood patterns, vehicle type, distance, journey duration, and real-time demand metrics used to train self-learning algorithms