AI Sentencing Ethics + The Future of Algorithmic Sentencing

Artificial intelligence is upending many aspects of legal work. As a transcription aid, a research assistant, and an administrative helper, AI has already saved legal teams countless hours while improving the accuracy of case preparation. 

Yet, this fast-evolving technology is also entering far murkier ethical territory: criminal sentencing. And here, algorithms aren’t just assisting with paperwork — they’re influencing the course of a person’s life after conviction.

Ultimately, the challenge is to balance innovation with justice. The tools must support — not replace — the human judgement that’s so central to a fair and equitable legal system. And that balance is where the debate over AI sentencing is most intense.

What Is AI Sentencing?

AI sentencing is the use of artificial intelligence to guide or influence judicial decisions, often by predicting the likelihood of reoffending. These systems generate risk scores or recommendations that judges may consider when determining sentence length and other decisions.

What Is Algorithmic Sentencing?

Algorithmic sentencing is broader than AI sentencing, covering data-driven formulas and risk assessment tools of varying sophistication, and it’s designed to make outcomes more consistent. Both models force courts to confront issues of bias, transparency, and accountability.

How Is AI Used in Criminal Sentencing?

In criminal sentencing, AI is used to assess how likely someone is to commit another crime. It also informs decisions on bail, probation, or sentence length. Sentencing algorithms use data such as prior convictions, age, employment history, and even survey responses to assign risk scores, which courts may use as one factor among many during the decision-making process.

Supporters argue AI can reduce inconsistency and help overburdened courts work more efficiently. However, critics note that when algorithms draw on historical crime data sets, they may inadvertently reinforce the very biases the justice system is intended to correct. This tension between efficiency and fairness makes AI sentencing one of the most debated uses of technology in the legal field today.

Looking Back at Tech in Court

Courts have long experimented with technology to enhance the efficiency and consistency of sentencing. Early tools relied on structured formulas, weighing factors like prior convictions, age, or employment status to generate sentencing recommendations. These systems were meant to reduce bias, but instead, they often turned complex human stories into rigid numerical outcomes.

One of the most well-known examples is Correctional Offender Management Profiling for Alternative Sanctions, better known as COMPAS, a risk assessment tool developed in the late 1990s. Used in several states, including parts of Wisconsin, Florida, and New York, COMPAS assigns defendants a score estimating how likely they are to reoffend. Judges consider these scores when determining bail, probation, and sentencing.

While some argued that COMPAS added objectivity, critics quickly pointed out its flaws. A 2016 ProPublica investigation found the tool was almost twice as likely to mislabel Black defendants as high-risk compared to white defendants, sparking a national debate over bias in algorithmic justice and the criminal justice system as a whole.

Courts have grappled with these concerns directly. In State v. Loomis (2016), the Wisconsin Supreme Court allowed COMPAS to be used but warned judges not to rely on it exclusively, acknowledging the tool’s potential utility and its serious limitations. This case is still a touchstone for conversations about AI in courts.

These early experiments show the promise and risks of algorithmic sentencing: technology can streamline overloaded systems, but it can also perpetuate existing problems if left unchecked. 

Ethical Concerns in AI & Algorithmic Sentencing

In this context, it’s clear that AI sentencing is not just another efficiency tool. While transcription and case-prep technologies help lawyers save time, sentencing algorithms influence decisions that determine a person’s freedom (or lack thereof). That makes the ethical discussion around AI in courts far more urgent.

In practice, these tools raise difficult questions that cut to the core of justice — from whether algorithms reinforce biases to how transparent and accountable they can ever truly be. To truly understand what’s at stake, let’s look at the core concerns shaping this debate.

Sentence Bias

Bias is one of the most serious ethical issues in AI sentencing. The ProPublica investigation mentioned above demonstrates how tools like COMPAS can reinforce racial disparities rather than reduce them. 

Despite the promise of objectivity, these systems are only as fair as the data they learn from. When algorithms are trained on years of records that detail years of unequal policing and prosecution, they’ll repeat the same patterns.

This raises serious concerns for how AI will support the right to a fair trial or central principles like equal protection under the law. If, for example, certain groups are consistently labeled as “high risk” because of skewed data, it undermines the standards of fairness and equality that the justice system is supposed to uphold. 

This problem persists today. A 2024 Tulane University study found that while AI-assisted sentencing could reduce jail time for some low-risk offenders, minority defendants were still disproportionately flagged as high risk. The takeaway? Unless these issues are resolved, AI sentencing tools may only make unfair sentencing faster.

Transparency and the Black Box of Algorithmic Justice

Many of the tools used in courts are proprietary, leaving defendants and their attorneys unable to examine how a risk score was generated. This so-called “black box” problem undermines due process, as it’s difficult to challenge or appeal a decision when the reasoning behind it is hidden. 

The State v. Loomis case in Wisconsin highlighted this issue directly, with the court acknowledging the risks of relying on COMPAS while still allowing its use. France has been more explicit, banning the use of AI to predict judges’ sentencing decisions for fear of influencing outcomes.

Calls for transparency have only grown louder. Scholars and policymakers argue that defendants deserve to know how technology is influencing their sentence, and legal teams need the ability to interrogate the data behind the recommendations. That’s why tools like Rev emphasize full transparency, giving legal teams clear outputs they can review, question, and control, rather than leaving decisions to a black box.

Excessive Reliance on AI in Courts

AI sentencing tools are often presented as objective, and this creates a dangerous dynamic in issuing court rulings: Judges and parole boards may lean too heavily on algorithms instead of exercising their own well-developed judgment. This reduces sentencing to a data-driven “rubber stamp.”

Legal scholars warn that this over-reliance ignores what algorithms cannot capture — the broader context of a defendant’s life, the potential for rehabilitation, and deeper moral concerns. A young first-time offender, for example, could be labeled as “high risk” based solely on their ZIP code or peer group while overlooking elements like steady employment, family support, or participation in treatment programs.

As a study in Criminal Justice Ethics argued, AI systems are structurally limited in their ability to account for these factors. Yet in a field like criminal justice, sentencing must never be reduced to mere statistics.

Accountability in Algorithmic Sentencing

The further removed court decisions are from human judgment, the less clear it is who should be accountable when an outcome proves unjust. Is it the judge who relied on the tool, the court that adopted it, or the private company that designed it? When responsibility is so unclear, it’s difficult to assign liability. 

Courts have only begun to grapple with this gap. In State v. Loomis, the Wisconsin Supreme Court sidestepped the issue by cautioning judges not to rely exclusively on COMPAS, but it stopped short of defining who bears responsibility for its errors. This leaves a significant gray area where no one can be fully held accountable.

Efficiency vs. Justice

Algorithms can process massive amounts of data, apply consistent formulas, and reduce some of the delays that burden courts. For systems struggling with heavy caseloads, the promise of faster, more uniform sentencing can be very appealing.

But efficiency shouldn’t be confused with justice. Sentencing decisions carry moral weight that can’t be reduced to speed or statistics. If courts prioritize quick resolutions over careful deliberation, they rob defendants of their chance to be judged as individuals. Justice requires time and consideration, and tools that shortcut that must be weighed against the cost to public trust.

Are There Benefits to Using AI for Sentencing?

AI can be a helpful tool in criminal sentencing, particularly by improving consistency and limiting the time served for some low-risk offenders. The Tulane study found that AI-assisted sentencing helped cut jail time overall —  incarceration dropped by 16% for drug crimes, 11% for fraud, and 6% for larceny — a sign that these tools can ease pressure on crowded court systems.

Ultimately, AI’s potential lies in its ability to make decisions more consistently and reduce excessive sentences. These tools could also help courts focus their resources where they’re most needed and create more fair outcomes for low-risk defendants. With thoughtful development and responsible use, AI-assisted sentencing may help make the justice system more efficient and more humane.

Bringing Accountability Into AI Sentencing

To ensure these tools support justice rather than undermine it, courts and legal teams need guardrails that enforce fairness and transparency. Here are a few practical ways to bring accountability into algorithmic sentencing.

Apply Human Expertise in AI Criminal Justice

Human review is essential to using AI ethically for sentencing. As attorney Dr. Monroe Mann of Monroe Mann Law puts it, “AI can certainly sometimes offer solutions that human eyes miss, but it is still so new, and prone to error, that I would only feel comfortable with AI sentencing that is reviewed by a human.”

That means every AI-generated recommendation must be checked — and challenged if necessary — by a judge. Keeping a knowledgeable human-in-the-loop is the only way to preserve fairness and ensure the technology supports, rather than dictates, judicial decisions. 

It’s also the model for how AI can be adopted responsibly in law more broadly: transparent, reviewable, and under human guidance (much like the way Rev’s AI tools support efficiency without displacing judgment).

Make Algorithmic Sentencing Transparent

Sentencing tools must be explainable enough that defendants, attorneys, and judges can understand the basis of a recommendation. Rather than treating algorithms as proprietary black boxes, courts should demand models that can be audited and scrutinized. 

Explainability does not require exposing every line of code, but it does call for clear disclosure of what factors are considered, how they are weighted, and how accuracy is measured.

Conduct Bias Audits

In AI sentencing, fairness won’t happen automatically. It requires frequent testing and retraining of the machines behind the outputs. Regular audits should measure accuracy across demographic groups and flag disparities that may unfairly label certain defendants as higher risk (or as future criminals). 

Open-source toolkits such as Aequitas and FAT Forensics give courts and researchers ways to evaluate whether algorithms treat different groups equitably. They can reveal whether an algorithm is more likely to generate false positives for one demographic group, or whether it’s less accurate when applied across different communities

Requiring these types of regular audits reinforces the principle that algorithms are not above scrutiny. The goal is to keep sentencing tools accountable to the same standards of fairness expected of any other part of the justice system.

Develop Regulatory Standards for AI in Courts

AI systems are evolving quickly, and regulations are only starting to catch up. The EU’s new Artificial Intelligence Act, effective August 2024, classifies the use of AI in criminal justice as “high-risk,” subjecting it to rigorous requirements around transparency, oversight, and safety. In the U.S., the White House’s Blueprint for an AI Bill of Rights outlines principles like fairness, transparency, and human alternatives, which should guide any automated system with real-world impact.

Although current rules are still evolving, they provide a starting point. Courts and legal teams can follow these benchmarks by adopting obligations that go beyond technical audits. Algorithmic Impact Assessments (AIAs), which Canada already requires for federal agencies deploying AI tools, are a great example. 

These structured assessments evaluate potential harms and recommend safeguards before a tool is put to use. Paired with measures like public disclosures, they help limit bias and build trust in AI-assisted decision-making.

Educate Legal Teams on Algorithmic Justice

Given the complexity of AI legal tools, judges, prosecutors, and defense attorneys must understand how they work and how to spot signs of bias or error. Learning the basics helps ensure lawyers rely on their own judgment and push back when algorithms get it wrong.

Legal professionals must learn to ask the right questions: What data was this model trained on? How accurate is it across different groups? Can its recommendations be independently verified? Training, whether through resources from groups like the Partnership on AI or the AI Now Institute, is essential to keep human judgment central in AI sentencing.

What Does the Future of Algorithmic Sentencing Look Like?

In the years ahead, the debate around algorithmic sentencing will likely be defined by the same issues. Courts will seek more efficiency on one side, while advocacy groups demand fairness, transparency, and oversight on the other. Sentencing tools will grow more sophisticated, but so will the scrutiny around their use.

Practically speaking, this means AI will probably remain a supplemental tool rather than a decisive authority. Courts and legislators are already highlighting the importance of human oversight and calling for algorithms to be subject to audits, disclosures, and independent testing. The most likely path to progress involves refining these systems within stricter ethical and legal guardrails.

The ultimate trajectory will depend on how legal professionals engage. If courts adopt AI sentencing with care, it could help reduce inconsistencies and ease caseload burdens. If not, the threat is an automated system that replicates the same inequities it promised to solve.

How Courts and Legal Teams Can Use AI the Right Way

Whatever the type of work, courts and legal teams should operate under one basic guideline: Use AI (with care) where it strengthens accuracy and efficiency without displacing human judgment. 

That means reserving high-stakes decisions like sentencing for judges, while applying AI to supportive tasks such as transcription, case preparation, and document management. There, technology improves speed and precision while leaving moral and ethical questions to humans.

Tools like Rev show how this balance works in practice. Our legal transcription platform streamlines depositions and hearings, and even transforms hours of body camera footage into searchable transcripts that attorneys can review quickly and thoroughly. This approach ensures legal teams gain the benefits of innovation while staying within clear ethical boundaries — leveraging AI as an assistant, not an arbiter.

Building Trust in Legal Tech

AI technology is here to stay, and it will continue to shape the justice system, but it must do so on terms that preserve fairness and accountability. Courts, policymakers, and practitioners face difficult questions — ones that won’t be solved overnight. What is clear, however, is that the best path lies in using AI where it adds value without getting in the way of human judgment.

The good news? Legal teams don’t have to choose between practicing law efficiently and ethically. Tools like Rev help professionals save time and improve accuracy, while keeping human judgment and fairness at the center of their practice.

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