The Challenges of Data Analytics in Criminal Justice

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During the past couple of years, there has been a large increase in the number of companies and government agencies using data analytics to improve the criminal justice system. These data are important because they can help to uncover new insights into crime, which can aid in the identification and prevention of crimes.

However, there are many challenges to using these types of tools. For example, it can be difficult to assess how effective an agency’s data are, because of the lack of a clear, consistent method of collecting, analyzing, and reporting data.

In addition, the data can sometimes be inaccurate, because it may not be representative of the population the agency is trying to serve. Ultimately, this can make it difficult to create a strategic plan that will lead to positive results.

Defining the problem

The new wave of data analytics in criminal justice has the potential to deliver new ideas on the appropriate response to crime. But there are some snags. This article explores three structural challenges associated with using predictive modeling in criminal justice.

The first is the ethical and practical questions relating to the use of this new technology. There are concerns that algorithmic calculations may not be calibrated correctly given the initial facts. In addition, algorithms may be subject to biases and inaccuracies.

Using machine learning and big data, courts can more accurately predict which offenders are likely to recidivate. However, some algorithms have a tendency to discriminate against lower-income groups. These concerns have led to calls for increased transparency and regulation of the application of such algorithms.

A second concern is an effect that such algorithms have on the fairness of the outcomes they produce. For instance, a false-positive ranking on an individual’s chances of reoffending could interfere with their fundamental liberties.

Oppositional frame

One of the best ways to sort the good from the bad is to use an oppositional frame to analyze your data. This process will not only reveal subjugated voices, but also the hidden nihilism that permeates a lot of modern organizations. As a result, you’ll be able to see the true strengths of your organization’s data and make better use of it.

While most organizations have their own unique take on the opposing frame, there are a few standard practices that you’ll want to adopt as you begin the process of analyzing your own data. Some of these are common sense, others not. For instance, you’ll likely need to take into account a few key factors before you can get to the bottom of a given crime. The same applies to figuring out a suspect’s motives.

Informational frame

When using information analytics for criminal justice, one must understand the frame that he or she is working within. Frames can be used to help identify criminals, and they can also be used to help law enforcement agencies determine which cases to investigate.

Data analytics is a process that combines hundreds of data points to find patterns and trends in a system. The purpose is to uncover previously unseen patterns. It also helps law enforcement agencies create profiles of specific criminals.

A study of academic abstracts has revealed how informational frames are formed in criminal justice. One abstract, for example, argued that the rise of datafication in criminal justice is unbalanced.

In the context of big data analytics in criminal justice, the following three frames are suggested. These are the optimist, the neutral, and the oppositional.

Solutions to crime control potential

The use of data analytics in criminal justice is a critical element of crime control strategies. Data scientists have the ability to glean previously unseen patterns and make correlations between hundreds of data points. This can lead to the effective profiling of criminals. It is also useful for identifying crime hot spots and optimizing patrol dosage.

However, there are some concerns about the harms of using data. For instance, the potential for ‘black box’ decision-making based on biased data can lead to biased outputs. Moreover, the linkage of data to official decisions may trigger unwarranted state intervention.

As a result, it is important to distinguish between the three frames that are commonly employed to explain the use of big data analytics in criminal justice. In this article, we explore these three frames and their implications for the future of criminal justice.

Kevin Peter