AI and Crime: How Algorithms Are Reshaping the Criminal Justice System

Published on: July 28, 2026 | 11 minutes read

By: Alliant University

Forensics

AI Chip In Handcuffs For Artificial Intelligence Crime Law. 3d rendering

In February 2024, Chicago ended its contract with ShotSpotter, an AI-powered system designed to detect and locate gunfire.^[1] While supporters argued that the technology helped police respond more quickly to potential shootings, critics questioned whether algorithm-driven tools should influence public safety decisions.

Today, AI tools are being used throughout the criminal justice pipeline. The appeal is easy to understand: Algorithms can process large amounts of information far faster than humans and identify patterns that might otherwise go unnoticed. At the same time, criminal justice decisions involve social context and ethics that no algorithm can fully understand on its own.

The reality is that AI is neither a simple solution nor a clear threat. Its impact depends largely on how it is used, who oversees it, and whether human professionals remain actively involved in interpreting its findings.

Key Takeaways

AI tools like predictive policing software, facial recognition, and risk-assessment algorithms are now active at every stage of the criminal justice process, from investigation to sentencing.
Algorithmic bias, lack of transparency, and due-process concerns are documented problems, not hypothetical ones, that forensic professionals must be equipped to navigate.
Human expertise is not a backup to AI. It is the essential layer that gives AI-generated evidence legal standing, ethical grounding, and interpretive meaning in court.

Where AI Enters the Criminal Justice Pipeline

Artificial intelligence is no longer confined to a single stage of the criminal justice process.

In law enforcement, predictive policing systems use machine learning to analyze historical crime data to identify geographic areas where certain offenses may be more likely to occur.
At the same time, facial recognition technology can compare surveillance footage against large image databases to assist investigations, while digital forensics platforms apply analytics to help investigators sort through enormous volumes of phone records, emails, social media activity, and digital evidence that would take humans far longer to review manually.
AI also appears later in the process. Some agencies use AI behavioral profiling models to identify potential patterns associated with criminal activity.

Not all AI systems operate the same way, however. Some tools function as what researchers call “glass box” AI. These models are relatively transparent, allowing users to see which factors influenced a prediction. Because of this, they are generally easier to defend in legal settings.

Others operate as “black box” AI systems. These models may generate highly accurate predictions, but the path from input to output can be difficult to explain, even for the people who designed them. In a courtroom, that lack of transparency creates obvious challenges.^[2]

The rapid growth of AI in criminal justice reflects how quickly these technologies are becoming embedded in public safety systems. Industry estimates placed the U.S. AI-in-law-enforcement market at approximately $3.5 billion in 2024, with continued growth expected throughout the next decade.^[3]

Future forensic professionals are now expected to understand not only criminal behavior and investigative methods, but also how these AI technologies and algorithmic systems generate recommendations.

#1 Predictive Policing and the Hotspot Problem

One of the earliest and most widely adopted uses of AI and crime involves predictive policing. These systems analyze large volumes of historical crime data to identify locations, time periods, or patterns that may be associated with future criminal activity.

In practice, this often takes the form of hotspot policing. An algorithm reviews years of reported incidents and identifies neighborhoods where certain crimes are statistically more likely to occur. Law enforcement agencies, such as police departments, can then use those predictions to guide patrol deployment, surveillance efforts, and resource allocation.

Supporters argue that these tools help departments make better use of limited personnel, expanding their AI capabilities for resource planning. Critics, however, point to a fundamental problem: predictive systems learn from historical data, not objective reality.

If a neighborhood has experienced a heavier police presence for years, it will naturally generate more arrests, reports, and enforcement data.
That information then becomes part of the training data used by future algorithms.
Researchers have warned that this creates a feedback loop in which historically over-policed communities continue receiving disproportionate attention, regardless of whether underlying crime rates justify it.

The debate becomes even more heated when predictive systems move beyond locations and begin evaluating people. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) system is one of the most widely discussed examples. Designed to estimate the likelihood that an individual will reoffend, COMPAS has been used in various jurisdictions to inform decisions related to pretrial release, sentencing, and supervision.^[4]

The larger concern, here, is that judges, attorneys, and policymakers may place too much confidence in a score without fully understanding how it was generated.

#2 Facial Recognition, Digital Forensics, and Evidence Integrity

Facial recognition systems can compare images from surveillance footage against databases containing millions or even billions of photographs. In some investigations, this allows investigators to generate leads within minutes rather than spending days manually reviewing images.

Yet speed does not guarantee accuracy. Research shows that facial recognition systems can perform differently across demographic groups, whether it is age, sex, race, image quality, or the specific algorithm being used.^[5]

With that in mind, forensic investigators must treat facial recognition as an investigative lead rather than definitive proof of identity. The same principle applies to digital forensics.

Modern investigations often involve enormous volumes of electronic evidence, including:

Emails
Text messages
Phone records
Cloud files
Social media activity
Location data
Internet search histories

Here, AI-driven forensic platforms can help investigators identify relevant evidence that might otherwise remain buried inside millions of data points.

However, identifying evidence is only the beginning of the process. Investigators must still:

Verify authenticity
Establish a chain of custody
Confirm context
Determine whether information has been altered, manipulated, or misinterpreted before it can withstand scrutiny in court

#3 Risk Scores, Sentencing, and the Due-Process Debate

Perhaps no application of AI generates more controversy than risk assessment.

Across the United States, some jurisdictions use algorithmic risk scores to help inform decisions related to bail, sentencing, probation, parole, and pretrial supervision. However, this is a widely debated use case.

Many risk-assessment systems rely on proprietary software developed by private companies. As a result, defendants and their attorneys may see the final score but have limited access to the underlying logic used to generate it. This creates a difficult legal question: If a risk score influences a person’s liberty, should that person have the right to fully examine and challenge how the score was produced?

For many forensic and legal professionals, the solution is to establish a clear framework and define the goals for these tools.

Risk scores can provide useful information. They can identify patterns that deserve attention and help professionals process large amounts of data more efficiently. However, they cannot (and must not) replace the human judgment required to evaluate context, behavior, credibility, motivation, and fairness.

The Bias Embedded in the Data

When people hear the term “algorithmic bias,” it is easy to imagine a technical malfunction or programming mistake. In reality, bias often enters long before an algorithm begins making predictions.

Artificial intelligence systems learn from data. In criminal justice, that data frequently comes from:

Arrest records
Incident reports
Court outcomes
Case histories

Beyond simply documenting crime, these records reflect decades of policing and policy decisions. As a result, an algorithm trained on historical criminal justice data may inherit the same disparities that existed when that data was collected.^[6]

For example, imagine two neighborhoods with similar rates of illegal activity. If one neighborhood historically received a heavier police presence, officers would likely document more incidents, make more arrests, and generate more enforcement data there. An algorithm reviewing those records later may interpret the neighborhood as higher risk, not necessarily because more crime occurred, but because more policing occurred.

The problem becomes even more complicated when feedback loops emerge.

Consider a predictive policing system that identifies a particular neighborhood as a crime hotspot. In response, a department increases patrols in that area. More officers on the ground naturally lead to more observations, stops, citations, and arrests. Those new incidents are then added to the dataset, reinforcing the system’s belief that the neighborhood requires additional attention, and the cycle repeats itself indefinitely.

This does not mean the algorithm is intentionally discriminatory. The issue is that the system may struggle to distinguish between actual increases in crime and increases in police presence.

For forensic professionals, the solution to this challenge is clear: A risk score, predictive model, or AI-generated recommendation should never be treated as neutral simply because it comes from a computer. Responsible forensic practice requires questioning the quality of the underlying data and evaluating how conclusions were reached.

Preparing to Work at the Intersection of AI and Criminal Justice

As artificial intelligence becomes more common throughout criminal justice systems, one misconception continues to surface: that better algorithms will eventually reduce the need for human expertise.

Recent guidance suggests the opposite. The U.S. Department of Justice has repeatedly emphasized that AI should support decision-making, not replace it. Similarly, the Council on Criminal Justice has argued that human oversight must remain central whenever AI tools are used in law enforcement or legal proceedings.^[7]

The future of forensic work is understanding technology well enough to question it. Professionals working in this space increasingly need four core competencies.

First, they need a strong foundation in behavioral science. Criminal behavior, decision-making, trauma, deception, human motivation, and the relationship between mental health and criminal justice remain central to forensic work regardless of how advanced technology becomes.
Second, they need methodological literacy. Understanding how algorithms are built, what data they rely on, and where errors can emerge is becoming an essential skill.
Third, they need ethical reasoning, because questions involving privacy, bias, transparency, and due process cannot be solved through software alone.
Finally, they need strong communication and expert witness skills. Even the most sophisticated analysis has limited value if it cannot be explained clearly to investigators, attorneys, judges, juries, or policymakers.

As criminal justice systems become more data-driven, the need for professionals who can evaluate and interpret technology responsibly continues to grow.

The MS in Forensic Behavioral Science at Alliant University is designed for exactly this environment. In this program, students explore topics directly relevant to today’s forensic landscape, including:

Criminal behavior
Research methods
Ethics
Investigative decision-making
The application of evidence-based tools within justice systems

Just as importantly, students learn from faculty members who bring active field experience into the classroom. The program’s online format, combined with virtual residencies, also makes advanced forensic training more accessible for working professionals. Students can continue building their careers while pursuing graduate education without relocating or stepping away from existing responsibilities.

If you are interested in understanding how technology, behavioral science, and criminal justice intersect, request information about the MS in Forensic Behavioral Science at Alliant University.

When you are ready to take the next step, visit the application page to begin your journey.

The Algorithm Needs a Human Voice

Artificial intelligence can process millions of records, identify hidden patterns, and generate predictions in seconds. What it cannot do is determine what those predictions mean in the context of a real person’s life.

AI is a powerful tool, but tools require skilled hands. When the outcomes involve liberty and public safety, those hands must belong to professionals who understand both human behavior and the ethical responsibilities that come with interpreting evidence.

The next generation of forensic professionals will need the judgment to know when to trust an algorithm, when to challenge it, and when human expertise should carry greater weight than a machine-generated prediction.

At Alliant University, the MS in Forensic Behavioral Science prepares graduates to work in a criminal justice system where technology is becoming more influential, but human judgment remains indispensable.

Ready to take the next step? Request more information from an admissions counselor at Alliant today.

Sources:

^[1] Ebrahimji, Alisha. “Critics of ShotSpotter gunfire detection system say it’s ineffective, biased and costly.” CNN. February 24, 2024. https://edition.cnn.com/2024/02/24/us/shotspotter-cities-choose-not-to-use. Accessed June 30, 2026.

^[2] Liu, Xiaoming, Danni Huang, Jingyu Yao, Jing Dong, Litong Song, Hui Wang, Chao Yao, and Weishen Chu. “From black Box to glass Box: A Practical Review of Explainable Artificial Intelligence (XAI).” AI. November 3, 2025. https://doi.org/10.3390/ai6110285. Accessed June 30, 2026.

^[3] Zheng, Kena. “Antitrust in artificial intelligence infrastructure – between regulation and innovation in the EU, the US, and China.” Computer Law & Security Review. October 7, 2025. https://doi.org/10.1016/j.clsr.2025.106211. Accessed June 30, 2026.

^[4] Fazel, Seena, Matthias Burghart, Thomas Fanshawe, Sharon Danielle Gil, John Monahan, and Rongqin Yu. “The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies.” Journal of Criminal Justice. August 2, 2022. https://doi.org/10.1016/j.jcrimjus.2022.101902. Accessed June 30, 2026.

^[5] Kitharidis, Sofoklis, Anthonie Schaap, Thomas Bäck, and Niki Van Stein. “Bridging racial and age Gaps in face Recognition: A Data-Augmentation Framework for Fair AI.” SN Computer Science. April 4, 2026. https://doi.org/10.1007/s42979-026-04885-x. Accessed June 30, 2026.

^[6] Ramos-Maqueda, Manuel, and Daniel L. Chen. “The data revolution in justice.” World Development. November 18, 2024. https://doi.org/10.1016/j.worlddev.2024.106834. Accessed June 30, 2026.

^[7] Yen, Rachel. “DOJ report on AI in criminal justice: Key takeaways – Council on Criminal Justice.” Council on Criminal Justice. January 6, 2025. https://counciloncj.org/doj-report-on-ai-in-criminal-justice-key-takeaways/. Accessed June 30, 2026.