Police AI Bias: Safeguards and Oversight

Published: 24 February 2026. The English Chronicle Desk. The English Chronicle Online.

A senior police AI chief has acknowledged that crime-fighting artificial intelligence carries inherent bias risks. The focus on AI comes as law enforcement across England and Wales considers expanding its use to tackle modern criminal threats more efficiently. Alex Murray, director of threat leadership at the National Crime Agency and national lead for AI, confirmed that a new national police AI centre would prioritise recognising and reducing these biases wherever possible. He emphasised that while AI can offer extraordinary advantages for policing, careful oversight is essential to avoid perpetuating unfair outcomes.

Bias in policing AI typically emerges when algorithms rely on historical datasets that reflect prior human prejudices. This can lead to certain groups being unfairly targeted, or individuals misidentified based on race, gender, or socioeconomic status. Murray stressed the importance of understanding these risks before deploying AI tools, saying that officers must be trained to interpret outputs responsibly and mitigate errors. The approach, he explained, combines rigorous data preparation, appropriate model training, and extensive testing to reduce bias to acceptable, manageable levels.

Retrospective facial recognition, an AI-powered tool used after crimes occur, has already highlighted the challenges of bias in policing. This technology compares a suspect’s image to a database of faces collected over time, and there have been documented cases where the system produced skewed results. Live facial recognition, which scans crowds in real time, is even more controversial due to its potential for public intrusion, although it is less widely deployed. A report released last December revealed that retrospective systems were sometimes used without adequate safeguards, raising significant ethical and operational concerns.

The Association of Police and Crime Commissioners (APCC) criticised past failures in sharing these issues with affected communities and sector stakeholders. Darryl Preston, the APCC’s forensic science lead and Cambridgeshire’s police and crime commissioner, said that discovering bias—even in limited circumstances—demonstrates the need for independent oversight. He added that technology must be thoroughly tested before deployment to ensure fairness, an obligation that had not been fully met in previous cases.

The £115 million national AI centre aims to address these shortcomings, providing a centralised mechanism for evaluating AI tools and determining which products from private suppliers are suitable for police use. Currently, each force in the UK decides independently, a process seen as inefficient and inconsistent. Murray described this move as essential for keeping pace with criminals, who are increasingly using AI in imaginative ways to evade detection or commit offences. One example involved a paedophile claiming that child abuse images were deepfakes, forcing police to disprove the claim to secure a conviction.

Murray rejected simplistic comparisons between AI in policing and science fiction concepts like “Minority Report” predictive policing. He noted that AI’s benefits extend across a wide range of investigations, from social media monitoring to digital device analysis, yet the final decisions must remain with trained human officers. AI’s capacity to streamline searches for vehicles linked to suspects, accelerate manhunts, and process large volumes of CCTV footage could save hundreds of hours of detective work, he explained.

The practical benefits have already become evident in recent operations. In Luton, four suspects linked to attacks and thefts from cashpoints were apprehended and quickly prosecuted thanks to AI-assisted analysis. Investigators uploaded data from phones in Romanian, and AI automatically translated the content, identified relevant material, and compiled the evidence into a format suitable for prosecution. This reduced a process that might have taken months into just a few weeks, highlighting the transformative potential of AI in evidence collection.

Trevor Rodenhurst, chief constable of Bedfordshire Police, said that frontline officers have grown increasingly receptive to AI as they experience its advantages firsthand. “They are no longer suspicious; they are asking when they can have it,” he explained. This shift in perspective suggests that AI tools may become standard practice, especially as efficiencies in data analysis, threat identification, and case preparation become more apparent.

Despite the promise of these technologies, Murray stressed that bias remains a key concern. He outlined ongoing efforts to work with data scientists and engineers to clean datasets, train models, and implement safeguards that minimise errors. Even with these measures, he acknowledged that some bias is unavoidable, making officer training and awareness essential components of ethical AI deployment. Maintaining public trust requires transparency, careful testing, and a clear commitment to mitigating unintended consequences.

Looking ahead, the police AI centre will play a crucial role in both operational efficiency and accountability. By centralising AI oversight, the initiative aims to ensure consistent standards across forces while evaluating emerging technologies in real time. This approach should prevent smaller forces from adopting tools that could inadvertently perpetuate bias, while fostering collaboration between technical experts and law enforcement professionals. Murray argued that such coordination is essential to harness AI’s full potential safely.

In addition to procedural improvements, AI could help tackle emerging crime types that challenge traditional policing. For instance, political agitators who spread false imagery on social media could be more quickly identified, while AI analysis of digital devices may uncover hidden networks of criminal activity. By reducing manual workloads, officers can devote more attention to strategy, community engagement, and operational decision-making. While AI enhances investigative speed, human judgment remains indispensable for interpreting results and taking action responsibly.

The debate over AI in policing highlights the delicate balance between technological innovation and ethical responsibility. While AI offers transformative capabilities, missteps in implementation risk eroding public confidence and exacerbating inequalities. The new national AI centre represents a proactive effort to manage these challenges, promoting transparency, fairness, and efficiency. Murray’s vision underscores the belief that AI is a tool for enhancing human policing, rather than replacing critical decision-making.

As the UK continues to expand AI integration into law enforcement, the spotlight remains on accountability and fairness. Effective safeguards, independent oversight, and continuous officer training are essential to prevent misuse and ensure equitable outcomes. By confronting bias head-on, police leaders aim to establish a model for responsible AI deployment that maximises public safety while maintaining trust. The coming years will reveal whether these measures can successfully balance innovation with ethical governance, offering lessons for law enforcement worldwide.

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