Government Risk Profiling: Discrimination Risks Remain High

Margriet Vermeer · 2026-05-07

Government risk profiling is a tool used to identify potential threats, but without careful safeguards, it can lead to serious harm. A recent study from the Universiteit van Amsterdam highlights a critical issue: when authorities use data to predict behavior, they risk discriminating against certain groups. This isn't just a theoretical problem. It's a real concern for communities already facing bias. ### The Core Problem: Unchecked Data Use Risk profiling often relies on algorithms that analyze personal data. The goal is to flag individuals who might commit crimes or pose security risks. But the problem is that these systems can inherit biases from the data they're trained on. For example, if historical arrest data shows over-policing in minority neighborhoods, the algorithm might unfairly target those same communities. The study argues that without "principled measures," this cycle of discrimination continues.  ### Why This Matters for Social Justice For professionals working in racism, politics, and social issues, this is a familiar challenge. The government's use of predictive tools can undermine trust. When people feel they're being watched or judged based on their race, zip code, or social status, it deepens divides. It also raises legal questions about civil rights and equal protection under the law. ### What Needs to Change The researchers suggest several steps to reduce harm: - **Transparency**: Agencies must explain how they build and use risk models. - **Oversight**: Independent bodies should audit these systems for bias. - **Accountability**: There must be clear consequences when profiling leads to discrimination. - **Public Input**: Communities affected by profiling should have a voice in policy decisions. These aren't just technical fixes. They're about fairness. Without them, the risk of harm stays high. ### A Real-World Example Consider a scenario in a major US city. Police use a risk score to decide who to stop and search. The algorithm flags more people from low-income neighborhoods. But those areas are also where police presence is highest. So the system creates a feedback loop: more stops lead to more data, which leads to more stops. This isn't just inefficient. It's unjust. ### The Bigger Picture Risk profiling isn't going away. Governments will keep using data to make decisions. But the question is how they do it. The Universiteit van Amsterdam study reminds us that without strong principles, the tools meant to protect us can end up hurting the most vulnerable. For professionals in this field, the takeaway is clear: we need to push for policies that prioritize equity over efficiency. ### Final Thoughts This isn't about rejecting technology. It's about using it responsibly. The conversation around risk profiling is part of a larger debate about privacy, power, and prejudice. By staying informed and advocating for change, we can help build systems that are fair for everyone.