Artificial intelligence (AI) in criminal justice has been pervasive for some time and will only grow in the future. AI’s impact on the criminal justice system in 2023 is immense, with significant influence on crime monitoring and prevention, judicial and correctional systems, and many other criminal justice practices. It’s not difficult to spot AI’s impact on public safety and criminal justice, from traffic safety systems to crime forecasts, criminal pattern recognition, and more. Even those who never expect to encounter the system are impacted by the use of AI in criminal justice in some fashion.
Examples of Artificial Intelligence in Criminal Justice
Criminal justice professionals continue to increase their use of AI “to profile people, ‘predict’ their actions, and assess their risk . . . such as committing a crime, in the future.” Much of AI’s function in criminal justice is based on using pattern recognition to detect and predict criminal activity, but that is not its only use. Let’s look at some additional ways AI is used.
Law enforcement, specifically, utilizes AI through video and image analysis. Current technology allows police to go beyond simply identifying people and objects. It allows them to detect complex accident and crime scenes—both while in progress and after the fact. Recent technological advances have also resulted in the ability to improve the detection of an individual’s face captured with poor image quality, at imperfect angles, or even when their face is obscured. One method for identifying low-quality images is degrading clear images of numbers and letters to emulate the lower-quality images, then using degraded mathematical representations to make identifications.
Since its introduction in the late 1980s, forensic DNA evidence has been a game changer in the criminal justice world—equally useful in solving cold cases as it has been in exonerating wrongfully convicted prisoners. The science and accuracy behind today’s DNA analysis, however, are hardly recognizable from their early days, which has much to do with the use of AI.
Today, AI allows forensic labs to “detect and process low-level, degraded, or otherwise unviable DNA evidence that could not have been used previously.” This includes the ability to detect extremely small amounts of DNA and extract usable DNA from evidence that even predates testing. To address the problems associated with detecting multiple DNA donors, data mining and AI algorithms are capable of deciphering large amounts of complex data, and, ultimately, separate and identify individual DNA profiles.
Research laboratories, such as Cadre Forensics, use AI algorithms for gunshot pattern signatures to differentiate muzzle blasts, determine timings, assign shots to specific firearms, and estimate identifying probabilities. Additionally, AI sensors that can identify and pinpoint gunshots can be installed in municipal infrastructures, such as buildings and streetlights. These sensors, which capture the timing and sound of gunshots, and assist in determining the shooter’s location, allow police to arrive at a scene without ever being summoned.
Perhaps AI’s most notable change on law enforcement is the ongoing shift from being reactionary to being proactive in their efforts to prevent crime. Decades of sophisticated crime data, along with new technologies, allow law enforcement agencies to gather and interpret “connections and patterns in financial records, geospatial imagery, surveillance camera footage, social media data, public records, news feeds, and many other open and proprietary sources” to understand crime patterns better. Having a greater understanding of the particularities in time, location, and demographics allows agencies to better allocate their scarce resources. Adjusting when, where, and how to deploy their personnel and instruments can significantly reduce crime.
AI use is no less prevalent in the courtroom. Courts worldwide use AI to take pressure off overburdened court dockets, analyze legal data, and make sentencing recommendations. Researchers are attempting to “design computer programs that can perform legal reasoning and assist attorneys in solving legal problems” to increase the speed, quality, and specificity of legal statutory interpretation. AI tools are already being used by judges to make decisions about bail, sentencing, and even whether to incarcerate or release suspects that await trial.
Other Uses of AI in Criminal Justice
Criminal Justice agencies utilize AI in myriad other ways. A few of these include:
- Traffic safety systems used to catch violators (such as red-light cameras), and increase traffic safety through automatic traffic accident detection systems.
- Interpretation of radiological images to assist medical examiners with establishing causes and manner of death.
- To “predict and recognize anomalous patterns and to learn to recognize new patterns” to assist with fraud detection.
- To predict potential elder abuse victims.
- To reduce criminal recidivism.
- To detect evidence captured in crime scene photos.
- To recognize bomb components.
The Future of Artificial Intelligence in Criminal Justice
The use of AI in criminal justice is not likely to slow. Through improving existing technologies and discovering new ones, AI will likely shape how humans interact with the criminal justice system throughout the 21st century.
Advances in automated object and activity detection through drones and satellite imaging will significantly impact law enforcement’s ability to recognize and prevent crime through movement and pattern analysis. Integrated facial recognition—technology used to detect individuals in multiple locations across multiple cameras—will assist investigators with suspect identification. Drones and robotics technology also have the potential to provide law enforcement with context and situational awareness, in real time, as they are called on to respond to crimes.
Also, with contemporary citizens offering significantly more private information to the masses, through social media and the like, criminal justice professionals have access to a mountain of information on us that they didn’t have on our predecessors. This data, with the assistance of AI, will help law enforcement detect crimes that otherwise may have gone undetected.
Risks and Ethical Concerns
Perhaps, not surprisingly, increased capacity for and dependency on AI have led to concerns by many, including errors in facial recognition, lack of accountability, biased systems affecting judicial fairness, privacy rights, and more.
One of the more controversial tools is criminal risk assessment algorithms. Risk assessment algorithms consider a defendant’s profile and compute a recidivism score for courts to consider when determining sentencing. The concern is that the historical crime data inputted into these tools detects crime-related patterns, rather than root causes—that is, correlation instead of causation. The fear is that the data used reflects current social inequities and will only exacerbate an already unequal system.