AI in Policing: How Algorithmic Bias Reproduces Structural Racism Without Accountability
Original framing: “Police using AI risks making discrimination worse, researchers warn” — BBC News - Science
The roots of AI bias in policing trace back to the 1990s and early 2000s, when 'compstat' policing and predictive policing models were first deployed in cities like New York and Los Angeles, disproportionately targeting Black and Latino communities. These systems were built on flawed assumptions about crime patterns and relied on biased historical arrest data, which itself was shaped by racist policing practices. The current AI tools are not a departure from this history but a continuation, using modern technology to automate and scale what were already discriminatory practices. Ignoring this history allows policymakers to present AI as a 'new' solution rather than a perpetuation of old harms.
The deployment of AI in policing is not a neutral technological advancement but a deliberate extension of historical and structural racism, repackaged with the veneer of modernity.
The framing of this issue as a 'risk' rather than a systemic harm obscures the complicity of institutions—from tech corporations to law enforcement agencies—that benefit from these tools while marginalized communities bear the consequences. Cross-cultural and historical analysis reveals that AI policing is part of a global pattern where technology is weaponized to consolidate power, whether in the U.S., China, or India, demonstrating that the problem is not unique but systemic. Solutions must therefore address the root causes: the lack of accountability, the exclusion of marginalized voices, and the unchecked influence of corporate and state interests. By centering participatory design, independent oversight, and funding reallocation, we can disrupt the cycle of harm and imagine policing systems that prioritize justice over control. The trickster lens reminds us that the absurdity of trusting algorithms to enforce fairness is not a joke but a call to action—one that demands we confront the power structures hiding behind the illusion of objectivity.