Predictive AI in Law Enforcement
Predictive AI in Law Enforcement: Failing Communities While Misallocating Resources
By Melody Peace
September 29, 2025
Artificial intelligence (AI) is increasingly being integrated into law enforcement, particularly through predictive policing tools designed to forecast where criminal activity might occur. While intended to enhance public safety, current implementations of predictive AI often misdirect resources, inflate crime statistics, and fail to reduce actual crime.
The Promise—and the Reality—of Predictive AI
Predictive policing offers several potential advantages in theory:
Efficient Resource Allocation: AI can analyze crime patterns to suggest where officers should patrol (Police Chief Magazine).
Proactive Crime Prevention: Forecasting “hotspots” aims to allow police to intervene before crimes occur (SmartDev).
Yet, the most valuable evidence of predictive AI’s failure is its inability to reduce crime rates. While Law Enforcement Agencies over-police communities designated by AI, often those historically targeted through bias policing—crime statistics become artificially inflated. At the same time, actual criminal activity often goes unnoticed in under-patrolled areas, as officers, under the pressure of public scrutiny or political agendas, divert resources elsewhere. This combination of over-policing and unsolved crimes disproportionately inflates crime statistics into astronomical numbers, creating the illusion that crime is worsening. In reality, crime itself is not increasing; the perception of rising crime is largely a product of our over-reliance of predictive AI software designed built on statistics obtained from bias policing.
The Human Cost
Predictive AI in Law Enforcement at its current state perpetuates systemic inequities. The result: innocent civilians are profiled, and every encounter carries a heightened risk of escalation, with officers feeling the need to justify their actions during every encounter with use of excessive force.
Meanwhile, other areas with legitimate need for policing experience delayed response times, fewer patrols, and inadequate investigative attention, allowing crime to go unresolved. From both an ethical and operational perspective, this represents a major failure of current AI implementation.
Economic and Resource Implications
Financially, predictive AI wastes an incomparable number of resources. Law Enforcement Agencies relying on predictive software risk investing into areas that are historically overpoliced, while the need for real investigative resources compounds in neglected areas. The inefficiency most often results in supplemental allocation of funds, exacerbating the fiscal budget and public mistrust. The reallocation of funds from critical community development programs such as education and social services towards Law Enforcement agencies only intensifies existing inequities. The diversion of resources from essential programs also negatively affects crime rates used in the framework of predictive AI software, underscoring the urgent need to implement predictive policing strategies that are both effective and equitable.
Practical Solution: Altering the Framework of Predictive AI
Exploring a few actionable steps could begin to address these problems:
Focus on financial loss: Prioritize resources in areas where unchecked crime or delays in response result in the greatest economic loss.
Restructure Predictive Data: Use ethically sourced, representative data rather than relying solely on historical crime statistics from over-policed neighborhoods.
Redesign Patrol Allocation: Base patrols on emergency call patterns rather than algorithmic “hotspots” alone.
Increase Presence in Surveillance Deserts: Expand visibility in areas traditionally under-monitored to ensure actual crime is identified and investigated.
Implementing effective reforms allows predictive AI to allocate resources effectively toward improving public safety without perpetuating inequity or unnecessarily wasting taxpayer dollars.
Moving Forward: Research and Accountability
To ensure predictive AI is used responsibly, agencies must invest in further research, oversight, and equity-focused protocols:
Independent Audits: Regular review of AI systems for bias, fairness, and effectiveness.
Transparency: Public access to algorithms and datasets to allow scrutiny.
Community Engagement: Involving local stakeholders in AI deployment ensures alignment with public needs and values.
Ethical Data Standards: Only use data that accurately represents crime patterns, avoiding inflated statistics from over-policed neighborhoods.
In summation, Predictive AI has the potential to improve law enforcement—but at its current state, it’s misallocation of personnel and resources inflates crime perceptions, and exacerbates inequities. By restructuring data, focusing more on minimizing financial loss, and aligning patrols with real community needs, law enforcement agencies can begin to leverage AI responsibly, enhancing safety while maintaining fairness and trust.
References
Amnesty International. (2025). UK use of predictive policing is racist and should be banned, says Amnesty. Retrieved from https://www.theguardian.com/uk-news/2025/feb/19/uk-use-of-predictive-policing-is-racist-and-should-be-banned-says-amnesty
Summary: This report, titled Automated Racism, criticizes predictive policing tools for relying on biased data derived from practices like stop-and-search, disproportionately targeting Black individuals. It calls for a ban on such technologies, arguing that they modernize racial profiling and exacerbate systemic inequality. This source supports multiple points in the article about over-policing, bias, and inequitable outcomes.Rodriguez, F. S. (2025). How AI is Setting the Stage for a Digital Jim Crow Era. Congressional Hispanic Caucus Institute. Retrieved from https://chci.org/wp-content/uploads/2025/03/Rodriguez_Fara_Predictive-Policing-How-AI-is-Setting-the-Stage-for-a-Digital-Jim-Crow-Era-.pdf
F.S. Rodriguez discusses how predictive policing algorithms, by using proxies like zip codes, replicate and digitize historical racial segregation, leading to systemic biases in law enforcement practices.
The Markup. (2023). Predictive policing software terrible at predicting crimes. Retrieved from https://themarkup.org/prediction-bias/2023/10/02/predictive-policing-software-terrible-at-predicting-crimes
An analysis by The Markup reveals that predictive policing software, such as Geolitica, has a success rate of less than 1% in accurately predicting crimes, raising concerns about its efficacy and reliability in law enforcement.
The Markup. (2023). How We Assessed the Accuracy of Predictive Policing Software. Retrieved from https://themarkup.org/show-your-work/2023/10/02/how-we-assessed-the-accuracy-of-predictive-policing-software
This article outlines The Markup's methodology for evaluating the accuracy of predictive policing software, highlighting concerns about the tools' effectiveness and potential biases.
