Government Risk Profiling: Discrimination Risks Remain High

Margriet Vermeer · 2026-05-07

Government risk profiling is a tool used to predict and prevent crime, but it comes with serious ethical concerns. A recent study by the University of Amsterdam warns that without clear, principled measures, these systems can lead to discrimination and harm. Let's break down what this means and why it matters. ### The Core Problem The core issue is that risk profiling often relies on data that reflects existing biases. Think about it: if police data shows more arrests in certain neighborhoods, a risk model might flag those areas as high-risk. But that data doesn't account for over-policing or systemic inequality. The result? People from marginalized communities get unfairly targeted. This isn't just a theory. Studies show that algorithms used in criminal justice can perpetuate racial disparities. For example, a 2016 ProPublica investigation found that a popular risk assessment tool was biased against Black defendants. It falsely labeled them as future criminals at nearly twice the rate of white defendants.  ### Why Principled Measures Matter The University of Amsterdam study emphasizes that without "principled measures," the risk of discrimination stays too high. What does that mean in practice? - **Transparency**: Governments must explain how these systems work and what data they use. - **Accountability**: There should be independent oversight to catch biases early. - **Fairness**: Models must be tested for disparate impact before deployment. These aren't just nice-to-haves. They are essential to protect civil rights in an increasingly automated world.  ### Real-World Consequences When risk profiling goes wrong, the consequences are real. People can be denied jobs, housing, or loans based on flawed predictions. In the criminal justice system, it can mean longer sentences or higher bail. These aren't abstract numbers—they affect real lives. Consider this: a single mother in Detroit might be flagged as high-risk because of her zip code. She then faces extra scrutiny from child protective services, even though she's done nothing wrong. That's the human cost of biased algorithms. ### What Can Be Done? There are steps we can take to make risk profiling fairer: - **Use diverse data**: Include input from communities most affected by profiling. - **Regular audits**: Have third-party experts check for bias annually. - **Public input**: Let citizens review and challenge profiling models. The goal isn't to eliminate risk assessment entirely. It's to ensure it serves justice, not prejudice. ### A Call for Caution The University of Amsterdam's warning is timely. As governments rush to adopt AI tools, we must slow down and think critically. A few key questions to ask: - Who benefits from this system? - Who is harmed? - How do we measure fairness? Without answering these, we risk building a high-tech version of old-fashioned discrimination. ### Final Thoughts Risk profiling isn't going away. But we have a choice: use it responsibly or let it deepen existing inequalities. The research is clear—principled measures aren't optional. They are the only way to avoid harm. Let's push for policies that put fairness first. Our communities deserve nothing less.