By Stuart Kerr, Technology Correspondent
Published: 21 July 2025
Last Updated: 21 July 2025
Contact: liveaiwire@gmail.com | Twitter: @LiveAIWire
Author Bio: About Stuart Kerr
In an era where predictive models forecast crime and machine learning tracks misconduct, artificial intelligence is no longer just a tool for law enforcement—it’s becoming its watchdog. But as algorithms assume more responsibility in scrutinising those in power, the question arises: who, or what, polices the police?
AI is quietly stepping into one of society’s most controversial roles—not only predicting criminal activity but also detecting patterns of abuse and misconduct within the very institutions designed to uphold justice. What begins as a quest for accountability could easily slide into opaque surveillance, unchecked automation, or even digital scapegoating.
Predicting the Predictors
Predictive policing has long captured public imagination, and concern. From software like PredPol to new AI-powered systems, law enforcement has leaned on data to forecast where crime might occur. Yet these same tools are increasingly being redirected inward—used to monitor officer behaviour, flag unusual patterns in complaints, and audit internal practices.
The shift is subtle but profound. AI systems are being tasked with identifying potential bias in arrests, racial profiling trends, or even patterns of excessive force. This isn’t science fiction—it’s already happening. In cities like Los Angeles, Chicago, and London, machine learning models have been tested to assess officer conduct and provide early-warning alerts to internal affairs divisions.
But these models don’t operate in a vacuum. They rely on historical data—much of which may already reflect systemic injustice.
AI in Law Enforcement: The Delicate Balance explores these tensions in greater depth, examining how police departments walk a tightrope between innovation and intrusion.
Algorithms and Internal Affairs
The promise of algorithmic oversight is tantalising: impartial, fast, and immune to favouritism. But real-world deployments suggest caution is warranted.
In 2024, the Law Commission of Ontario published a landmark report on AI use in Canadian law enforcement, highlighting both its potential and the inherent risks. The report warned that unchecked algorithmic oversight could replicate existing biases, pointing to the fact that most misconduct data originates from internal reporting systems—systems already vulnerable to underreporting or suppression.
A similar theme runs through findings from the Yale Law School’s Media Freedom and Information Access Clinic, which documented how predictive tools often focus on low-level offences in already over-policed communities. In this context, AI isn’t neutral—it’s potentially reinforcing the very dynamics it aims to fix.
The Silent Bias: How AI Tools Are Reshaping Justice adds to this discussion, revealing how invisible assumptions within training data can skew accountability metrics.
Fairness, Bias, and the Digital Gavel
Perhaps most controversial is facial recognition technology (FRT), which is increasingly deployed to monitor public interactions and internal procedures. While some departments use it to track suspects, others are turning the same lens inward—reviewing footage of police stops to identify unprofessional conduct.
But the tech is far from perfect. A 2025 investigative report by Wired revealed that FRT was involved in over 70% of recent arrests in several U.S. states—yet often went undisclosed in court. A growing chorus of legal scholars warns that such surveillance carries real risks: not just false positives, but also the erosion of procedural fairness.
This concern has deepened in light of findings from Criminal Legal News, which revealed a $1.5 million settlement after officers were wrongly accused due to an AI misclassification—a stark reminder that algorithmic error can be just as damaging as human prejudice.
Invisible Infrastructure: AI’s Hidden Role in the Modern World reinforces this theme: that unseen systems, however well-intentioned, can become dangerously opaque when not carefully governed.
Trust, Transparency, and the Road Ahead
The future of AI in internal policing may rest on a single value: trust. Without transparency in how models are trained and deployed, even the most sophisticated oversight tools risk alienating the very public they aim to protect.
Clear regulatory frameworks, independent audits, and public reporting mechanisms will be critical to ensuring these systems serve justice rather than undermine it. As AI and the Gig Economy has shown, algorithmic power without accountability often benefits institutions more than individuals.
Ultimately, when machines watch the watchers, society must ask a difficult question: are we witnessing progress, or outsourcing judgement to an unblinking eye?
About the Author
Stuart Kerr is the Technology Correspondent for LiveAIWire. He writes about artificial intelligence, ethics, and how technology is reshaping everyday life. Read more