Pros and cons of predictive policing

Pros and cons of predictive policing

Predictive policing is a concept and practice in law enforcement centered on the use of data analytics and relevant technologies to determine potential criminal activity or threats and thereby, prevent crime from transpiring.

Note that a thorough definition has been discussed in a separate article. Nonetheless, this specific article explores and discusses the advantages and disadvantages of predictive policing.

The Pros: Advantages or Benefits of Predictive Policing

1. Improves Crime Prevention

The primary selling point of predictive policing centers on the prevention of crime before it happens based on data-driven and technology-centric approaches. It specifically involves data analytics, as well as other technologies such as artificial intelligence or machine learning and Big Data applications, to provide law enforcers with relevant insights and targets for intervention, as well as process optimization and automation.

Several studies have reported the effectiveness of predictive policing in crime prevention. In an article published by the U.S. Federal Bureau of Investigation, its implementation in Santa Cruz, California over six months resulted in a 9 percent drop in burglary incidents while in Los Angeles, the accuracy of its results was twice that of conventional practices.

2. Informed Decision-Making

Note that data analytics has the primary purpose of discovering useful information to support decisions. When applied to law enforcement, particularly through predictive policing, data analytics promote informed decision-making across numerous facets such as the analysis of crime patterns and prevention of crime, predicting risks and determining identities of offenders, and identifying the vulnerabilities of a community or its members.

In her TED Talk, Anne Milgram, a former attorney general of New Jersey and a criminal justice reformer, discussed how the introduction of technology-centric data analytics in Camden has helped law enforced make better decisions as regards arrest operations, identifying criminal offenders, and prosecution and sentencing of offenders.

3. Advances the Justice System

Another benefit that comes from predictive policing centers on how it advances the criminal justice system. Note that the practice promotes informed decision-making. What this means is that it not only prevents or reduces crimes but also improves both the quantity and quality of arrests, as well as support how law enforcers determine the criminal liability of suspected offenders.

Milgram explained that prior to the introduction of data analytics in Camden, 70 to 80 percent of the arrests made in the city were for low-level crimes. Note that the city ranked as the most dangerous city in the U.S. However, through data analytics, the city and the state, in general, began investigating and prosecuting offenders of heinous crimes, street gangs, drug and gun traffickers, and corrupt politicians.

The Cons: Disadvantages and Limitations of Predictive Policing

1. Privacy and Security Concerns

Remember that some of the advantages of data analytics and Big Data application are also some of the advantages of predictive policing. However, the same is true for its disadvantages or drawbacks. The fact that the practice depends on the collection and processing of data has raised concerns over privacy rights. Some of these data might be too personal, or their handlers might lack the capabilities and professionalism to keep them secured.

L. Edwards and L. Urquhart explored the privacy issues raised in collecting data for law enforcement. In their review study, they questioned the extent at which agencies can collect and process the social media data of Internet users for profiling purposes, while also raising concerns about the role of these law enforcers in protecting the digital identities of the citizens.

2. Issues About Accuracy

Although predictive policing is a data-driven and technology-centric approach to preventing crimes and improving the operations of law enforcement agencies, its reliability depends on the quality of the data, as well as the integrity of its implementers or users. A report from the RAND Corporation noted that the practice is not a crystal ball that can accurately foretell the future.

Some studies have investigated the effects of malpractices in predictive policing. For example, K. Lum and W. Isaac explored what would happen if a system is trained using biased data. The results magnified the biases committed by law enforces through conventional processes, thus increasing further disproportionate policing, especially in historically over-policed communities.

3. Leaning Toward Discrimination

Similar to the issues about privacy and accuracy, another criticism of predictive policing centers on how it can produce biased results. The American Civil Liberties Union and other advocacy and trade organizations criticized the practice for its tendency to proliferate racial profiling. As noted by Lum and Isaac, when fed with biased, a key limitation of predictive policing is that it magnifies biases emerging from conventional processes.

Another disadvantage of predictive policing is that it reduces the accountability of law enforcers. As explained in the study by L. B. Moses and J. Chan, because most of the processes in data analytics are automated, it could undermine the ability of individuals or agencies to give an account of their decisions in significant ways.

FURTHER READINGS AND REFERENCES

  • American Civil Liberties Union. 2013. “Statement of Concern About Predictive Policing By ACLU and 16 Civil Rights Privacy, Racial Justice, and Technology Organizations.” ACLU. Available online
  • Edwards, L. and Urquhart, L. 2016. “Privacy in Public Spaces: What Expectations of Privacy Do We Have in Social Media Intelligence?” International Journal of Law and Information Technology. 24(3): 279-310. DOI: 10.1093/ijlit/eaw007
  • Friend, Z. 2013. “Predictive Policing: Using Technology to Reduce Crime.” FBI Law Enforcement Bulletin. Available online
  • Lum, K. and Isaac, W. 2016. “To Predict and Serve?” Significance. 13(5): 14-19. DOI: 10.1111/j.1740-9713.2016.00960.x
  • Milgram, A. 2013. “Why Smart Statistics Are the Key to Fighting Crime.” TED Talk. Available online
  • Moses, L. B. and Chan, J. 2016. “Algorithmic Prediction in Policing: Assumptions, Evaluation, and Accountability.” Policing & Society. 28(7): 806-822. DOI: 10.1080/10439463.2016.1253695
  • Perry, W. L., McInnis, B., Price, C. C., Smith, S., and Hollywood, J. S. 2013. Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. RAND Corporation. DOI: 10.7249/RR233