What changed

The Dutch data protection authority imposed a €824.99 million fine on Uber. According to the regulator, between 2018 and 2022 the platform used systems that suspended driver accounts, sometimes permanently, without adequate information and without a person checking for possible mistakes.

The decision concerns automated processing: software analyzes data and makes a decision without meaningful human participation. Uber disputes both the findings and the size of the fine, says the reviewed practices are historic, and states that its current processes include human review and an opportunity to appeal; the company plans to challenge the decision.

The lesson is not that every automated process is improper. It is that machine speed also multiplies the effect of poor data, incomplete rules, or signals interpreted without context when the outcome can interrupt someone’s income.

Why this matters

For a platform driver, losing account access can mean instantly losing the tool used to earn a living. In other industries, similar decisions can stop a payment, reject a credit application, remove a candidate from hiring, or deny access to an essential service.

In Europe, the rule invoked in the case protects people from fully automated decisions with legal or similarly significant effects, except in specific circumstances. In Brazil, the data protection authority notes that the LGPD gives people a right to request review and explanations of the criteria and procedures used; in practice, human review is meaningful only when the reviewer understands the case, can see relevant information, and has real authority to change the outcome.

Consider a fraud system that freezes an account after detecting an unusual location change. Without context, it may treat legitimate travel as a risk; without an explanation and a way to challenge the result, the person cannot correct the data or show what happened. Operational efficiency then becomes a human and reputational cost.

Reliable data comes before automation

Every decision system depends on what it receives: ratings, history, location, documents, and risk signals. Data cleansing means finding duplicates, stale records, inconsistent fields, and missing context before those records feed rules or models. It is not simply tidying a database; it reduces the chance that an operational error becomes real harm.

Organizations also need to know where each data point came from and record how it contributed to a decision. That trail helps teams investigate false positives — legitimate cases incorrectly flagged as problems — compare outcomes across groups, and explain the result in language a person can understand.

The greater the consequence, the higher the quality threshold should be. A content-ranking system may tolerate lightweight corrections; a system that changes income, credit, health, or access to rights needs testing, monitoring, documentation, and fast paths to reversal.

What companies can learn

Governance should not arrive only after the automation is finished. A Technology Cell can bring product, data, operations, security, legal, and support together to decide what may be automated, what requires human confirmation, and how affected people will be informed.

The goal is not to slow every innovation, but to decide where speed is safe. When an organization combines cleansed data, clear limits, and genuine human review, automation gains something a model cannot provide alone: legitimacy in situations that affect people.

• Map automated decisions and classify the impact of each one.
• Validate data quality, origin, and context before automating.
• Explain relevant factors and provide an accessible challenge process.
• Give human reviewers the information, time, and authority to correct outcomes.
• Monitor errors, reversals, and unequal effects over time.