Commercial artificial intelligence tools have recently emerged that are able to produce police reports. Some police departments have already adopted this technology. Also, some individual officers are using publicly-available AI tools. If AI could greatly reduce the time spent producing police reports, this could either substantially reduce the cost of policing, or free up police officers for other work. However, if the resulting reports are inaccurate, incomplete or biased, or if the process leaks confidential information, this could undermine the criminal justice system and harm citizens, perhaps causing an innocent person to be charged with a crime while the actual criminal is overlooked. At this time, both the benefits and the risks are poorly understood.  

Yet, despite the uncertainty, each of the more than 18 thousand law enforcement agencies in the U.S. must make its own decision about the use of AI. These agencies do not have the expertise or resources to assess whether any of the AI-based products on the market are right for them, and if so, what training, departmental policies and deployment strategies are needed to use the technology both safely and effectively.

This memo proposes fostering innovation in AI for policing without sacrificing safety through a combination of centralized actions by the U.S. Department of Justice and independent actions by state and local law enforcement agencies. The Department of Justice, through its National Institute of Justice, should establish a new research and evaluation program that will give state and local government agencies the information they need to make the best decisions about use of AI for police reports given their own needs and resources, and keep Congress and the Department of Justice abreast of AI use in policing nationwide as well. Each state and local agency should use this information to devise its own strategy, addressing issues such as whether to adopt AI, officer training, technology choice, budget, transparency, and other policies and procedures to use the technology where it is safe and effective.

While this memo focuses on use of AI for police reports, the recommended solution serves as a model for other AI use cases as well. Similar problems occur every time a large number of local government agencies are contemplating the use of AI in scenarios where the pros and cons are poorly understood, and there is potential for significant harm.  

Challenge and Opportunity     

Why Police Departments are Considering AI for Police Reports

Police reports are a cornerstone of law enforcement. These reports serve as the official record and generally the only written record of significant interactions between police officers and individuals, including arrests, crimes reported, and car crashes observed. The contents of police reports can influence important decisions, such as whether an individual is charged with a crime. When police officers testify in court about an incident that occurred months or years earlier, they typically rely on the police reports that they wrote soon after the incident to get the details right. When insurance companies want to assess liability, their decisions often depend on police reports. When police officers are accused of misconduct, investigators study the relevant police reports. When compiling crime statistics on which policy decisions will be made, critical data comes from police reports. It is therefore important for police reports to be accurate, complete, and unbiased. 

Given the importance, it is no surprise that many police officers spend hours per day producing these reports. This comes at a cost. If the time spent on police reports could be reduced, then police departments could reduce the number of officers employed and thereby greatly reduce expenses, or reallocate officer time to other productive tasks, or some combination of the two. Many police departments in the U.S. are especially motivated now to free up their officers’ time, because there is a national shortage of qualified officers, and many departments have unfilled positions.

A number of companies have announced products that integrate AI into the writing of police reports. Some vendors such as Truleo and Axon have claimed that AI assistance can reduce the total time spent on police reports by 80% to 90%, which would yield tremendous cost savings if true.  In response to such promises, some police departments have already adopted this technology. Given financial and staffing pressures, more departments are likely to follow.  

But are the cost savings real? Are the reports produced when using AI reliable enough for their intended purpose? And what strategies for adoption will maximize both cost savings and report quality? Most police departments do not have the AI expertise on staff to answer those questions. Indeed, roughly three fourths of law enforcement agencies in the U.S. have fewer than 25 police officers, and thus very few IT professionals.

How AI Would Be Used

The general idea is that information about the incident is fed into an AI-based system which produces a draft report of what a particular police officer did and observed, which that officer must review. The details vary from one AI-based product to another. In some cases, police officers feed this information into the system by typing relevant facts on a computer. In others, officers participate in an interactive oral interview with the system. In the most ambitious system, the AI system is fed information about an incident by uploading recordings from a body-worn camera, with no direct involvement from the officer. These systems transcribe the audio and use the resulting text; some analyze video as well. In all of these cases, once the AI-based system produces an initial draft, the officer inspects the draft, makes any changes he or she wishes, and signs off on the result.  

The Risks of Using AI for Police Reports are Poorly Understood

AI-based products for police reports use generative AI, where an AI system is trained from a set of prior examples to understand which words and phrases are frequently used together. The system can then generate entirely new text for new circumstances by using the relationships observed in its training in combination with some new input data and some elements that are entirely random to avoid repetition and unnaturally formulaic text. Regardless of the domain, producing text using generative AI can be problematic. 

First, generative AI can randomly produce “hallucinations,” i.e. information that is roughly consistent with the training data but incorrect in the current circumstance.

Second, when an AI model is trained on biased data, it produces biased results. For example, if reckless driving citations in the training data are more likely to involve alcohol with young drivers than with old drivers, then hallucinations involving alcohol may be more likely with young drivers.  Companies are rarely transparent about their training data sources, but some sources from law enforcement could easily be biased with respect to factors such as race, age and gender. 

Third, some generative AI models leak information in unexpected and often unseen ways.  For example, if the system uses new inputs from users to improve (or “train”) the model, then a new input may later be revealed to other users. This happens with the widely-used generative AI services that are offered for free to the public, and some officers already use those free tools. Even if new inputs are not used in this way, those new inputs could be transferred to a provider of AI-based services with weak defenses. If a police department allows its officers to use a system with inadequate protections, this would risk citizens’ privacy and possibly compromise future court cases. It is technically possible to design systems with better protection against leakage, but police departments typically have no way to tell which services have done so effectively. Given all of these risks, it is no surprise that some localities have sought to prohibit use of AI for police reports.

Of the various methods of putting information into the system described above, using recordings from body-worn cameras could save the most officer time, but it also brings additional risks that must be assessed. For example, when an officer in Utah uploaded the recording of an incident that occurred while a movie was playing in the background, the AI reportedly produced a police report claiming that the officer transformed into a frog. An error like that does no harm because it is easy to detect, but a different movie might have produced a far more dangerous error. Also, audio transcription is less reliable when people speak with accents or with an African-American Vernacular. Using AI to accurately turn video into text can be even more challenging. Finally, with this approach there is no opportunity to record an officer’s subjective experience before the officer is influenced by AI-generated text, which some people have argued is important. Testing is required to understand the seriousness of these potential risks, and any mitigation strategies.

In 2025, I organized a research project at Carnegie Mellon University (CMU) to investigate use of generative AI for police reports. We produced police reports using three different kinds of generative AI technology, and observed that material inaccuracies do occur. For example, in one assault case, an input to the AI indicated that the victim was not transported to a medical facility without providing a reason, but the resulting report inaccurately claimed that the victim refused transport to a medical facility. We also observed that error rates varied from one AI product to another, as well as from one type of police report to another, perhaps because some types of reports are more complex than others. Thus, it matters which AI technology a police department chooses and under what circumstances it directs its officers to use that technology.

As long as AI is only used to produce the first draft of a report, problematic text does not compromise report quality if the police officer finds this text and rewrites it before submitting the final report. That may or may not be sufficient. As explained by MIT professor David Autor and Alphabet Senior Vice President James Manyika, AI systems that augment humans without replacing them can fail if the AI is not designed to collaborate with humans, such as when human pilots could not prevent an Air France flight from crashing after the autopilot failed because the tool gave the pilots limited situational awareness. It is even less obvious, but the converse is also true: problems can occur if humans are not explicitly trained to collaborate with AI.

The CMU researchers conducted experiments in which experienced police officers were asked to make corrections to prewritten police reports which contained hallucinations, omissions, and “event swaps” in which things occur in the wrong chronological order. We observed that officers missed many problems, including those that might matter in legal proceedings, such as when a report incorrectly indicated that a suspect was holding a knife when encountered. It is important to note that this occurred in a university research exercise rather than a professional setting, and that the officers had never been explicitly trained to edit AI-generated text, i.e. to collaborate with AI. Better results might be possible in real police departments that have adopted the right kind of training, but this requires more investigation.

Even an error that is not directly material to the case can do harm.  A memo from the King County Prosecuting Attorney’s Office reports that, thanks to AI, “an otherwise excellent report included a reference to an officer who was not even at the scene. … And when an officer on the stand alleges that their report is accurate — they will be proven wrong…we do not want your officers certifying false police reports. The consequences will be devastating for the case, the community and the officer.” Defense attorneys can bring up this error every time that officer testifies for many years to come. 

The Benefits of Using AI for Police Reports are Poorly Understood

On the positive side, many departments would save money if AI reduced the amount of time that each officer spends on police reports by just tens of minutes per week. This reduction could be within reach. One prominent survey found that 62% of officers spend more than two hours per day on police reports and 14% spend more than four, and there have been news articles quoting police officers who said that time savings from AI were substantial, although this is anecdotal. Yet the most rigorous study to date did not find any reduction in time spent when AI was introduced. This issue also deserves more investigation. Moreover, the impact of AI on time spent and police budgets will vary greatly between departments, so a single one-size fits-all conclusion is inadequate.  Savings depend on factors like the number of police incidents per week, the types of incidents that are most common, and how pervasive technology already is in the department.

The benefits and risks associated with AI also depend on the deployment strategy. For example, police departments may choose to use AI in cases where time savings are great and risks are low, or when time savings are insignificant and risks are high. Departments may choose to use AI in a transparent manner in which problems are easily observed and quickly corrected, or in an opaque manner. Research could provide guidance to police departments on whether and how to adopt this technology while minimizing risks.

Unfortunately, this research will rarely occur under current policies. Individual police departments are unlikely to invest their limited resources into testing commercial AI software products, developing new officer training programs, measuring whether AI saves time or money, or collecting best practices for adoption. If the federal government fails to act, some states or cities may fund useful work. However, even the state and local agencies with the largest budgets, such as the New York City Police Department and the California Highway Patrol, have little incentive to bear the full cost of making new discoveries and then informing the nation’s 18 thousand law enforcement agencies, most of which are small and have needs and resources that are quite different. There are university researchers doing this kind of work, but very few, and most police do not read academic journals.  Informed decisions will only happen if the federal government takes action.

Plan of Action

Most of the actual decisions about whether police should use AI technologies at all, which specific AI technologies to acquire, and how those AI technologies should be used will be made by local officials. The specific decision-maker varies from locality to locality.  For most of these decisions, police chiefs are critical. They can weigh in directly on issues such as officer training and department policies governing technology use, or can delegate that role. In some jurisdictions, police departments make independent decisions about procuring technology such as AI, whereas in others municipal Chief Information Officers may play a more decisive role. It should be the responsibility of the federal government to inform these decisions, regardless of which state or local official has the final say in any locality. Thus, this memo will make actionable recommendations to two audiences: the federal Department of Justice, and those who make decisions for state and local law enforcement agencies.

Recommendation 1.  The Department of Justice, through the National Institute of Justice (NIJ) and in consultation with the National Institute for Standards and Technology (NIST), should create ongoing projects whose goal is to provide information to state and local agencies that helps these agencies make better decisions regarding use of generative AI for police reports.

The introduction of AI for police reports raises technical and operational questions that individual law enforcement agencies are poorly positioned to answer on their own. Addressing these questions falls within the mission of the National Institute of Justice (NIJ), the Department of Justice’s research and evaluation arm. NIJ is well positioned to generate and disseminate this evidence at a national scale, reducing duplication across thousands of agencies and enabling more consistent, evidence-based adoption decisions.

The NIJ should draw on expertise from multiple institutions to address these important questions.  Universities should play a central role, because the best academic researchers are accustomed to inventing entirely new methods that address novel challenges and emerging technologies. NIJ should therefore establish a funding program to support external research.  Others already work for NIJ, where understanding of the problem domain is deep, so important work can also be done internally. Although they typically lack law enforcement expertise, there are also experienced AI researchers at NIST’s Center for AI Standards and Innovation, so consultation with that center could help. Below are some examples of research that is needed.

Research on Evaluation Methodology for AI Products and Services      

A new methodology must be created that can assess AI-based products and services for police reports, and quantitatively determine their ability to produce reports that are both accurate and complete under a wide variety of scenarios. This methodology should also assess the risk of leaking confidential information.

Research on how to train police to edit AI-generated reports

Even when reports are generated by AI, it is the responsibility of a police officer to ensure quality through editing. Simply having a human involved does not mean that the report will be anywhere near as accurate or complete as if a human wrote it. Detecting and correcting subtle mistakes in text that someone else wrote is challenging, and few police officers have experience with the task. Extensive training may prove critical.  For example, officers might first learn enough about how AI-based tools work to dispel any illusions that they are infallible. Then officers might learn the types of mistakes that AI tends to make, which are different from the types of mistakes that humans tend to make. Research is needed to develop training strategies, and determine their effectiveness.

Research on Benefits and Costs of AI

The primary motivation for adopting AI is to save time and money. Do AI tools really reduce the time spent on police reports, and if so, by how much?  What are the lifecycle costs, including software, storage, IT support, and officer training? How do expected cost savings depend on factors that vary by police department, such as number of officers, the types of police report that are most common in the department, and existing IT infrastructure? How do they depend on technology choices, such as whether officers feed the AI by typing in information, participating in an audio interview, or uploading recordings from a body-worn camera?

Research on how departments can perform quality control

Any organization that introduces a technology with unknown impact should have a way of measuring quality in context on an ongoing basis, and not just before deployment.  How does a police department know if the reports generated with AI assistance are good enough, or if its officers are well-trained? One possibility might be to routinely assess the completed reports, such as by comparing AI-generated reports with video footage in a monthly audit as the Boulder Police Department tried or with officer-written reports as the Oklahoma City Police Department tried. Doing this as efficiently and effectively as possible may require a new method. Another might be to artificially inject errors of the kind that AI is likely to produce, and monitor whether injected errors are corrected. (One existing product from Axon already injects errors. Effectiveness may be limited because the injected errors are unlike those that AI is likely to produce, but this requires testing.)  If a few officers consistently submit reports with injected errors or other problems, this may indicate that those officers need further training.  If many officers consistently do so, then this may indicate a more systemic problem.  

Other types of research and analysis are perennial and therefore should generally be led by staff within NIJ, although outside researchers could play a smaller role. Outside researchers tend to be less effective when success requires the trust of law enforcement agencies, or when being consistently accurate is more important than inventing something new. Examples include:

• Assessment of products and services on the market today: Once the research described above produces a methodology to evaluate the quality of an AI product or service, that method should be applied to each product. DoJ staff should use that methodology to assess every new product or major update of an existing product that comes on the market, and the results should be made available to law enforcement agencies and municipalities across the country.This is comparable to the NIST program which tests facial recognition products.
• Identifying best practices and tracking use: With thousands of law enforcement agencies making independent decisions about the use of AI, it is inevitable that some will adopt better strategies than others. One ongoing mission of this program should be to collect information about what law enforcement agencies are doing with AI and its impact, both positive and negative.From this, they should produce a set of best practices which can be widely disseminated, and continually revise these best practices over time.  

All results and recommendations from this program should be made available directly to all of the 18 thousand law enforcement agencies in the U.S.The program should disseminate results to organizations that train police officers, including future police chiefs.This includes the FBI National Academy and state organizations like the California Commission on Peace Officer Standards and Training.It should also disseminate results through national organizations that serve state and local decision-makers, such as the National Association of Chiefs of Police, the Association of Public-Safety Communications Officials International, the U.S. Council of Mayors, and the National Association of State Chief Information Officers.

The program should also provide annual summaries of use of AI for police reports in the U.S. to Congress, the Department of Justice, and the general public, so it is possible to track trends over time and detect potential concerns before they become problematic.

Recommendation 2. Any state or local law enforcement agency that is seriously considering adoption of AI for police reports should first produce a strategic plan using information provided by NIJ, knowledge of local needs and resources, and other available information.

Without an appropriate strategy in place, the use of AI for police reports is likely to produce reports that fail to meet the needs of the criminal justice system, potentially putting innocent people at risk, and wasting taxpayer money. An effective strategic plan can mitigate these risks. This plan should address the following.

• Choice of AI technology: Police departments must choose products and services carefully, and incorporate DoJ recommendations from the research program above into existing procurement processes. For example, they should not procure an AI system in which the data that is entered can be used to train the model as this can make the data accessible and thus undermine privacy, or a system in which the risks of hallucination, omission or bias are deemed to be high. The National Association of State Procurement Officials should also include these recommendations in its guides. Given that individual officers can and already do use AI even when their department has not adopted it, departments should also explicitly prohibit officers from using publicly-available AI systems.
  
• Phased deployment: When rolling out any technology that carries risk, it is helpful for an organization to deploy in phases, such that each phase expands the extent of use.  After each phase, the organization must carefully assess whether deployment was successful before deciding whether to advance. Whenever problems are observed in these assessments, those problems must be addressed before proceeding. This has proven to be an important approach when adopting AI. In this case, that probably means initially using AI only for reports for which the consequence of errors is lower and/or the risk of inaccuracy is lower, e.g. traffic incidents rather than felony arrests.It also may mean initially using AI only by a small group of police officers who are already comfortable with the technology rather than scaling quickly to all officers.
  
• Transparency and oversight:The use of AI should be sufficiently transparent to stakeholders, the relevant legislative bodies (e.g. city councils, state legislatures), civilian oversight boards, and the community. For example, the policies and procedures about use of AI discussed above should be stated in advance and publicly accessible, as should plans for large-scale procurements. This is important both as a means of quickly detecting and correcting any problems that may emerge before they become serious, and for fostering community buy-in by keeping police use of AI consistent with public expectations and goals.  In addition, when a police officer uses AI-based tools to produce a report, there should be an indication on that report regarding what role AI played, something that some police departments have deliberately concealed.There should also be an indication of where the information that was fed into the AI system came from, e.g. whether that input includes recordings from body-worn cameras, audio from interactions with dispatchers, etc.This allows all stakeholders, including defense and prosecuting attorneys, to apply an appropriate form of scrutiny to the resulting police reports.
       
• Policies and procedures: Disasters are possible even with good technology. Police departments should establish effective policies and procedures before using AI for actual reports, also considering DoJ recommendations. This includes creating an effective training program for officers, where effective training goes well beyond just knowing the features of the software and addresses effective quality control. It also includes determining whether officers are allowed to see AI-generated content before they have written their own observations, given the “elasticity of human memory.”  Departments may adapt the process by which a police report is reviewed by people other than the author, e.g. by supervisors or experts. Departments should also define policies regarding which intermediate datasets used by AI software to produce police reports are retained, and who can access them. For example, if audio recordings are transcribed and summarized before generating reports, how long should the original recordings be retained?

Conclusion

In recent years, the capabilities of generative AI have advanced at an astonishing rate, leaving our understanding of how to make use of those capabilities far behind. This is particularly challenging for those who would like to use the potentially transformative capabilities of generative AI for producing police reports, and for other AI applications that share two qualities. First, there are dire consequences if use of the technology goes badly, such as the possibility that a flawed police report could lead authorities to charge the wrong person with a crime. Second, most of the decisions with significant impact are made by 18 thousand independent local government agencies with different needs and limited resources and AI expertise. It is hard to imagine how all of these agencies could make informed decisions regarding use of an emerging technology that is still poorly understood by tech-savvy institutions. 

Some agencies will avoid the risk by never even considering AI for a purpose like this.  However, they forgo any possibility of reaping potential benefits, such as a significant reduction in costs, or a reallocation of police time from paperwork to other productive activities. Other agencies will adopt AI, but in a way that does more harm than good, perhaps because they chose the wrong product or because they used it poorly. This paper proposes a two-pronged strategy that will give state and local decision-makers both the information they need to make good decisions, and the confidence that their decisions are right for their respective agencies.

The U.S. Department of Justice, through its National Institute of Justice, should establish a set of programs that all have the goal of providing actionable information to law enforcement agencies about use of AI for police reports. This includes the pros and cons of adopting the technology and how both vary from agency to agency, the strengths and weaknesses of AI products on the market, how to train officers in use of AI for police reports, how to perform continual quality control, and other best practices.   

Each state or local law enforcement agency that is considering AI for police reports should produce a strategic plan that makes use of information provided by NIJ. Topics in the strategic plan would likely include the types of AI that should and should not be used, a phased approach to adoption, a transparency strategy that makes it easier to identify issues before they become highly problematic, and other policies and procedures.

My thanks to my CMU colleagues who worked on a 2025 research project on AI and police reports: Dr. Aleecia McDonald, Dylan Bonanno, Kai Collins, Ayana Curto, Katie Eisenman, Madeline Falk, Jane Fleischman, Harrison Green, En Hung, Wendy Jiang, Lily Klucinec, Isabella Krisky, Skylar Lukic, Tzen-Chuen Ng, Nicholas Ortiz, Miguel Rivera-Lanas, Christopher Rodas Ochoa, Keya Sharma, Autumn Swartz, Morgan van der Linde, Maximilian Vieweg, Sophie Vincens, Kemp Winkler, Avi Wong.

Despite a $182 billion annual cost, the U.S. correctional system perpetuates itself: At least 95% of all state prisoners will be released from prison at some point, yet more than 50% of them reoffend within three years. 

A key driver of high recidivism is the systemic negligence of the carceral experience. While much attention is given to interventions post-release, rehabilitation inside correctional facilities is largely invisible to the public. This dynamic results in approximately 2 million incarcerated persons being locked in a “time capsule”—the world passes them by as they serve their sentences. This is a missed opportunity, as simple interventions like accessing educational resources and maintaining family contact during incarceration can cut recidivism by up to 56%. Reduced recidivism translates into more robust workforce, safer communities, and higher political participation. The new administration should harness the momentous bipartisan interest in criminal justice reform, audit the condition and availability of rehabilitative resources in prisons and jails, invest in digital and technology infrastructure, and sustainably end mass incarceration through building meaningful digital citizenship behind bars. 

Challenge and Opportunity

In the post-COVID-19 world, robust and reliable technology and digital infrastructure are prerequisites for any program and resource delivery. However, the vast majority of U.S. correctional facilities still lack adequate technology infrastructure, with cascading effects on the availability of in-prison programs, utilization of digital resources, and incarcerated people’s transition to the free world. 

As many other institutions quickly embrace new technology, prisons lag behind. In Massachusetts, prisons struggle to provide even basic rehabilitative, educational, and vocational training programs due to a shortage of hardware devices, such as tablets and Chromebooks, and insufficient staffing. Similarly, in Florida, internet access is constrained by legislation and exacerbated by a lack of funding. Many prisons are forced to limit or entirely cancel programs when in-person visits are inaccessible, due to either COVID-19 restrictions or simply insufficient transportation options for resource providers. Consequently, only 0.5% of incarcerated individuals are enrolled in educational courses. The situation is equally dire in juvenile detention centers from California to Louisiana, where poor access to educational opportunities contributes to low graduation rates, severely limiting future employment prospects for at-risk youths.

Despite these systemic challenges, there is a strong, bipartisan recognition of the need to improve conditions within the carceral system—and therefore a unique opportunity for reform. 

The Federal Communications Commission (FCC) has passed the most comprehensive regulations on incarcerated people’s communication services, setting rate caps for various means of virtual communications. Electronic devices, such as tablets and Chromebooks, are gradually being accepted in correctional facilities, and they carry education resources and entertainment. Foundationally, federal investments in broadband and digital equity present a generational opportunity for correctional facilities and incarcerated people. These investments will provide baseline assessment of the network conditions and digital landscape in prisons, and the learnings can lay the very foundation to enable incarcerated people to enter the digital age prepared, ready to contribute to their communities from the day they return home.

This is just the beginning. 

Plan of Action

Recommendation 1. Invest in technology infrastructure inside correctional facilities.

A significant investment in technology infrastructure within correctional facilities is the prerequisite to transforming corrections. 

The Infrastructure Investment and Jobs Act (IIJA), through the Broadband Equity, Access, and Deployment (BEAD) and Digital Equity (DE) programs, sets a good precedent. BEAD and DE funding enable digital infrastructure assessments and improvements inside correctional facilities. These are critical for delivering educational programs, maintaining family connections, and facilitating legal and medical communications. However, only a few corrections systems are able to utilize the funding, as BEAD and DE do not have a specific focus on improving the carceral system, and states tend to prioritize other vulnerable populations (e.g., the rural, aging, veteran populations) over the incarcerated. Currently incarcerated individuals are difficult to reach, so they are routinely neglected from the planning process of funding distribution across the country. 

The new administration should recognize the urgent need to modernize digital infrastructure behind bars and allocate new and dedicated federal funding sources specifically for correctional institutions. The administration can ensure the implementation of best practices through grant guidelines. For example, it could stipulate that prior to accessing funding, states have to conduct a comprehensive network assessment, including speed and capacity tests, a security risk analysis, and a thorough audit of existing equipment and wiring. Further, it could mandate that all new networks built or consolidated using federal funding be vendor-neutral, ensuring robust competition among service providers down the road. 

Recommendation 2. Incentivize mission-driven technology solutions.

Expanding mandatory access to social benefits for incarcerated individuals will incentivize mission-driven technology innovation and adoption in this space.

There have been best practices on how to do so at both the federal and state levels. For example, the Second Chance Pell restored educational opportunities for incarcerated individuals and inspired the emergence of mission-driven educational technologies. Another critical federal action was the Consolidated Appropriations Act of 2023 (specifically, Section 5121), which mandated Medicaid enrollment and assessment for juveniles, thereby expanding demand for health and telehealth solutions in correctional facilities. 

The new administration should work with Congress to propose new legislation that mandates access to social benefits for those behind bars. Specifically, access to mental health assessment, screening, and treatment, as well as affordable access to communication with families and loved ones on the outside, will be critical to successful rehabilitation and reentry. Additionally, it should invest in robust research focusing on in-prison interventions. Such projects can be rare and more costly, given the complexity of doing research in a correctional environment and the dearth of in-prison interventions. But they will play a big part in establishing the basis for data-driven policies.

Recommendation 3. Remove procurement barriers for new solutions and encourage pilots.

Archaic procurement procedures pose significant barriers to competition in the correctional technology industry and block innovative solutions from being piloted. 

The prison telecommunications industry, for example, has been dominated by two private companies for decades. The effective duopoly has consolidated the market by entering into exclusive contracts with high percentages of kickback and so-called “premium services.” These procurement and contracting tactics minimize healthy competition from new entrants of the industry. 

Some states and federal agencies are trying to change this. In July 2024, the FCC ruled out revenue-sharing between correctional agencies and for-profit providers, ending the arms race of higher commission for good. On a state level, California’s RFI initiative exemplifies how strategic procurement processes can encourage public-private partnerships to deliver cutting-edge technology solutions to government agencies.

The administration should take a strong stance by issuing an executive order asking all Federal Bureau of Prisons facilities, including ICE detention centers, to competitively procure innovative technology solutions and establish pilots across its institutions, setting an example and a playbook for state corrections to follow. 

Recommendation 4. Invest in need assessments, topic-specific research and development of best practices through National Science Foundation and Bureau of Justice Assistance. 

Accurate needs assessments, topic-specific research, development of best practices, and technical assistance are all critical to smooth delivery and implementation. 

The Department of Justice, through the Bureau of Justice Assistance (BJA), offers a range of technical assistance (TA) programs that can support state and local correctional facilities in implementing these technology and educational initiatives. Programs such as the Community-based Reentry Program and the Encouraging Innovation: Field-Initiated Program have demonstrated success in providing the necessary resources and expertise to ensure these reforms are effectively integrated. 

However, these TA programs tend to disproportionately benefit correctional facilities where significant programs are already in place but are less useful for “first timers,” where taking that first step is hard enough.

The new administration should work with the National Science Foundation (NSF) and the BJA to systematically assess and understand challenges faced by correctional systems trying to take the first step of reform. Many first-timer agencies have deep understanding of the issues they experience (“program providers complain that tablets are not online”) but limited knowledge on how to assess the root causes of the issues (multiple proprietary wireless networks in place). 

The NSF can bring together subject matter experts to offer workshops to correctional workers on network assessments, program cataloging, and human-centered design on service delivery. These workshops can help grow capacity at correctional facilities. The NSF should also establish guidelines and standards for these assessments. In addition to the TA efforts, the BJA could offer informational sessions, seminars, and gatherings for practitioners, as many of them learn best from each other. In parallel to learning on the ground, the BJA should also organize centralized task forces to oversee and advise on implementation across jurisdictions, document best practice, and make recommendations. 

Conclusion

Investing in interventions behind the walls is not just a matter of improving conditions for incarcerated individuals—it is a public safety and economic imperative. By reducing recidivism through education and family contact, we can improve reentry outcomes and save billions in taxpayer dollars. A robust technology infrastructure and an innovative provider ecosystem are prerequisites to delivering outcomes. As 95% of incarcerated individuals will reenter society one day, it is vital to ensure that they can become contributing members of their communities. These investments will create a stronger workforce, more stable families, and safer communities. Now is the time for the new administration to act and ensure that the carceral system enables rehabilitation, not recidivism.