What is predictive policing?
Predictive policing is a broad term for the use of statistical models and machine learning algorithms to try to forecast where crime is likely to occur, or in some more controversial applications, who is likely to be involved in it. The outputs of these systems are typically used to inform how police resources are deployed — which neighbourhoods get extra patrols, which individuals receive targeted outreach or intervention, which cases get prioritised for follow-up. The pitch from vendors is straightforward: use data to put officers where they are most needed, rather than relying on gut instinct or the inherited assumptions baked into traditional patrol patterns.
Place-based predictive tools are the most common variety. These ingest historical crime reports, calls for service, time and date patterns, and sometimes external data like weather, local events, or even social media activity to generate maps of forecast crime hotspots, typically refreshed daily or per shift. Person-based tools are a smaller but far more contentious category: rather than flagging locations, they score individuals, producing ranked lists of people assessed as being at elevated risk of committing or becoming victims of violent crime. It is the latter category that has attracted the most sustained criticism, and in several US cities, the most organised public opposition.
Where is predictive policing used?
The United States has been the most prolific adopter. PredPol, now rebranded as Geolitica, was for several years the best-known place-based system, deployed in dozens of departments from Los Angeles to Atlanta. ShotSpotter — which uses acoustic sensors and machine learning to detect and localise gunshots — has been rolled out across major US cities, though it has faced mounting questions about its accuracy and the disproportionate impact of its deployments on Black and Latino communities. The Chicago Police Department's Strategic Subject List, a person-based algorithm that assigned risk scores to individuals, was eventually decommissioned in 2020 following years of controversy and a damning report from the city's inspector general.
In the United Kingdom, adoption has been patchier and considerably more cautious in its public presentation, though several forces have quietly implemented tools that share the same basic logic. West Midlands Police developed and trialled its own Harm Assessment Risk Tool — HART — which used machine learning to classify suspects at the point of custody into low, medium, and high risk categories to inform decisions about pre-charge bail. Durham Constabulary used a similar tool called HART to inform custody decisions. These are risk-scoring tools rather than patrol-directing tools, but they rest on the same foundational idea: that an algorithm can usefully predict future behaviour from past data.
Kent Police was among the first UK forces to adopt place-based predictive software, working with the commercial product PredPol for a number of years. The system generated grid squares predicted to have an elevated risk of burglary, vehicle crime, and violence, and directed patrol resources towards them. Other forces have been interested observers without becoming committed adopters, partly due to cost, partly due to a lack of clear legal guidance, and partly — it is fair to say — because of reputational caution in a climate where the technology is publicly contested.
The feedback loop problem
The fundamental criticism of predictive policing is not that the maths is wrong, but that the data the models are trained on is not a neutral record of where crime happens — it is a record of where crime was previously detected and recorded, which is shaped as much by historical policing patterns as by underlying criminal activity. If a neighbourhood has historically been subjected to intensive stop-and-search, a high volume of arrests from that area will appear in the data. Feed that data into a model and it will predict more crime in that area. Send more officers there, make more stops and arrests, and the prediction appears to have been validated. The model has not found where crime is; it has found where policing has been.
This feedback loop is not merely a theoretical concern. The Chicago inspector general's report on the Strategic Subject List found that individuals who were assigned high risk scores were more likely to be stopped by police — which in turn increased their likelihood of appearing in the system's training data as people with police contact, which tended to increase their score further. The system, in effect, was partially measuring its own enforcement activity rather than actual criminal risk.
Critics also point to a deeper conceptual problem: most serious crime is comparatively rare, even in areas classified as high risk. A model that is accurate 90 per cent of the time at a population level can still generate enormous numbers of false positives when it is predicting something that affects a small minority. If one in a hundred people in a flagged area would actually go on to commit a violent offence, a 90 per cent accurate model will correctly identify nine of them — but it will also flag 90 people who would not. Every intervention based on the model's output involves a decision about what to do with those 90 false positives.
Bias, transparency, and accountability
Commercial predictive policing vendors have frequently resisted disclosing the details of their algorithms, citing intellectual property. This creates a significant accountability problem: it is very difficult to challenge a decision or assess its fairness when the logic behind it is opaque. Several US cities that adopted predictive tools struggled to obtain meaningful information about how they worked, even when subjected to public records requests. In the United Kingdom, the Algorithmic Transparency Recording Standard has been introduced as a voluntary framework for public bodies to publish information about algorithmic tools used in decision-making — but uptake among police forces has been slow and the standard is not yet mandatory.
Racial and socioeconomic bias in predictive policing outputs has been extensively documented in the US context, and there is meaningful evidence of similar patterns in UK deployments. A review of Durham Constabulary's HART tool found that postcode data used as a proxy for socioeconomic deprivation introduced bias against individuals from poorer areas, and raised questions about whether the tool was capable of complying with the Public Sector Equality Duty. The force modified the tool in response, but the episode illustrated how difficult it can be to build genuinely fair risk-scoring systems when the input data carries the imprint of structural inequality.
The current picture
Predictive policing is neither as transformative as its proponents claimed when the technology first emerged, nor as universally discredited as its harshest critics would suggest. The reality is messier and more contextual. Some place-based tools appear to produce modest reductions in certain crime types in certain conditions; others have shown no measurable effect when subjected to independent evaluation. Person-based tools have faced the most sustained opposition and several have been quietly wound down, though interest in them has not disappeared — it has shifted towards tools described as risk assessment or early intervention, which often share the same underlying logic under a less loaded name.
The legal framework governing predictive policing in England and Wales remains underdeveloped. There is no primary legislation specifically addressing algorithmic decision-making in policing, and while data protection law and equality duties provide some constraint, they were not designed with these tools in mind and their application is contested. This is an area where the law is moving considerably more slowly than the technology.
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