Key insights:

• • • The duty of competence now includes technology 鈥� Rules first written for legal knowledge and diligence are being read to cover the tools lawyers use, not just the arguments they make.

    • The rules didn’t anticipate AI, but they still apply 鈥� Formal opinions and updated guidance make clear that generative and agentic AI fall squarely within lawyers鈥� existing ethical obligations.

    • What reaches the court carries the highest stakes 鈥� A lawyer’s technology missteps are most consequential, and most visible, the moment they show up in a filing, an exhibit, or an argument made to a judge.

Every attorney holds a duty of competence, and that duty has always included legal knowledge and sound judgment. Today, however, it now extends to technology as well. Often called 鈥�technical competence鈥�, today鈥檚 duty of competence requires understanding the benefits and risks of the tools used in practice, not just the law itself.

The American Bar Association’s Model Rules of Professional Conduct describe an attorney’s duty of competence as the obligation to provide representation using the (Rule 1.1). Comment 8 of the rule states explicitly, in part: 鈥淭o maintain the requisite knowledge and skill, a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology鈥� .鈥�

Indeed, competence matters well beyond a lawyer’s own file. Every filing, brief, and exhibit is, in effect, a lawyer’s communication to the court, and the court relies on the presumption that what it receives has been prepared with care. When a lawyer misunderstands or misuses technology, whether that means citing an AI hallucination as precedent or mishandling e-discovery output, the failure lands directly on the judge’s desk.

Rules of professional conduct are written broad and durable, so they do not need constant revision. Even so, few could have anticipated the pace of technological change now reaching the legal field.

A lawyer cannot simply plead ignorance when technology goes wrong, and failing to understand the tools being used can lead to ethics complaints, sanctions, or malpractice claims. Perhaps even more damaging, it can just as easily undermine a judge’s confidence in everything else that lawyer submits.

Rules built to last meet a technology no one predicted

Each state and territory maintains their , but most draw heavily on the ABA’s Model Rules as a foundation. Precedent is also shaped by disciplinary proceedings and case law, including a growing number of cases in which judges have sanctioned lawyers for submitting AI-generated content that was never checked for accuracy.

Rules of professional conduct are written broad and durable, so they do not need constant revision. Even so, few could have anticipated the pace of technological change now reaching the legal field. E-discovery was an early example, already raising real questions about what a lawyer could certify to a court in good faith. The more significant shift today is generative AI (GenAI) and agentic AI, capable of producing legal analysis or even taking action with minimal human oversight, often producing material that looks polished and authoritative right up until a judge or opposing counsel checks it.

Recognizing this shift, the ABA issued in July 2024 on the use of GenAI tools in legal practice, confirming that the duty of competence applies squarely to AI. The Formal Opinion requires lawyers to understand these tools well enough to use them responsibly, supervise their output, and avoid overreliance on unverified results 鈥� a standard that matters most the moment a document is filed, or an argument is made in open court.

5 ways to stay ahead of the curve

Given how quickly legal technology is evolving, and how little margin for error exists once something reaches a judge, there are five practical ways that lawyers can maintain their technical competence, including:

1. 1. 1. Pursue CLEs and structured education 鈥� Continuing legal education courses focused on AI, e-discovery, and cybersecurity offer a reliable, credentialed way to stay current. Many state bars now offer, and some require, CLE credits specifically on legal technology, exposing lawyers to real-world case studies that include those involving AI missteps that have drawn judicial sanctions.
      2. Build personal familiarity through hands-on use 鈥� Reading about a tool is no substitute for using it. Treating AI as a “thought partner,” for drafting, brainstorming, or spotting issues, helps professionals develop an intuitive feel for what these tools do well and in which ways they fall short, long before a document ever reaches a court. This low-stakes experimentation on matters that don鈥檛 impact client confidentiality or sensitive data can help build practical AI fluency.
      3. Practice in sandboxes and controlled environments 鈥� Before relying on a new platform in an active matter, test it in a sandbox first. Many vendors and firms now offer walled-off spaces to explore a tool’s features and failure points without exposing real client data.
      4. Review organizational guidelines 鈥� Firms, courts, and bar associations increasingly publish their own AI use policies that cover permitted tools, disclosure requirements, and data security. A growing number of courts also require lawyers to certify or disclose any AI use in filings, making familiarity with local rules as important as firm policy.
      5. Choose the right tools for the task 鈥� Free consumer-grade AI tools may suit general research, but should not be used in matters involving privileged or sensitive information, let alone a court filing. Premium and fiduciary-grade platforms, built with legal-specific safeguards around data handling and auditability, are better suited for substantive casework. Matching the tool to the sensitivity of the task, rather than convenience, is critical.

Used together, these five habits can help lawyers build a durable foundation in AI literacy. While technology will keep evolving, of course, any lawyer who has internalized this approach will be far less likely to be caught off guard once their work is tested in front of a judge.

Why this is about more than just the rules

Maintaining technical competence is not simply about avoiding disciplinary consequence. It is about preserving what the legal system is meant to provide. When lawyers understand the technology they use, including AI, they can apply it to research and review in ways that benefit clients and courts alike.

Just as importantly, technical competence protects the public’s trust in the legal profession, and something more immediate: a judge’s ability to rely on what lawyers put in front of them. In the end, technical competence is not a burden imposed by the rules; rather, it鈥檚 what allows the relationship between lawyers and the court, and the profession itself, to keep working.

For more on AI in the courts, check out the 听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI)

Key insights:

• • • Judges can lead by example in responsible AI adoption 鈥� By engaging thoughtfully with AI and using it to enhance (and not replace) human judgment, judges can set an important standard for accountability, verification, and ethical use across the legal system.

    • AI excels at streamlining preparation, not decision-making 鈥� AI is most valuable for reducing the burden of preparatory tasks, such as summarizing briefs, organizing records, identifying questions, and clarifying technical or statutory language. It should not substitute for judicial reasoning, discretion, or decision-making.

    • Verification and guardrails are non-negotiable 鈥� Responsible AI use requires vetted tools, strong privacy protections, precise prompting, and independent verification of all AI-generated content. The integrity of judicial work depends on maintaining these disciplined processes.

There is a leadership opportunity sitting inside every courtroom in America, and most judges haven’t claimed it yet.

AI is already shaping the legal system, in how attorneys research, how clerks draft, and how litigants prepare their cases. In , the latest webinar from the听听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥斕鼵hief Justice Matthew Fader of Maryland observed that the 鈥渢echnology is just becoming ubiquitous.鈥�

鈥淚t’s spreading faster than any technology I’ve encountered in my lifetime,鈥� Chief Justice Fader said. The reality, of course, is that AI will influence a wide spectrum of legal processes, including how judicial work gets done. The question is whether judges will be passive observers of that shift, or active, thoughtful participants in it.

The advantages in engaging with AI extend beyond efficiency 鈥� it is also about leadership. Judges who use AI responsibly, with clear guardrails, careful verification, and a firm commitment to human judgment, can model the standard that the legal profession needs from the bench.

The preparation problem AI can actually solve

The bulk of the work for a successful hearing or trial is done long before the judge enters the courtroom. This happens through careful review of records, briefs, and complex subject matter. Yet time constraints often limit thorough preparation, and this is where AI adds real value.

When used appropriately, AI can streamline early-stage work without compromising judicial rigor. Judges can upload briefs and receive structured summaries of each party’s arguments, providing a clear roadmap before deep analysis begins. Transcripts can be searched instantly for specific testimony, and lengthy records can be organized by topic, which reduces manual review.

For more technical cases, AI can generate concise primers on unfamiliar subjects, helping judges engage expert testimony within the proper context. AI also can draft targeted oral argument questions, flag hallucinated or incorrect citations in briefs, identify factual inconsistencies, and create procedural checklists for motions or default judgments. It can even simplify complex statutory language to enhance the clarity of proposed jury instructions.

As Justice Linda Kevins of New York observed, the more creatively AI is applied, the more use cases emerge. Judges who begin with one small task often discover a range of new efficiencies, which transforms preparation from a burden to a strategic advantage.

Responsible use is the point

What separates a judge using AI as a thoughtful professional from one using it carelessly is the discipline built around it. And that starts before the first prompt.

Any tool a judge uses should be vetted by the court’s IT department, its administrative office, or both. The terms of service matter, such as whether prompts are retained, whether data is used for model training, whether confidential information could be exposed. These are not technical details to delegate, rather they are the minimum a judge should understand before uploading anything.

Prompting with intention matters too. Instructing AI tools to always cite its sources, to flag uncertainty, and to present arguments neutrally are habits that will produce better output and reduce the risk of erroneous data. Asking for authority behind every claim, and explicitly telling the tool not to guess, should become standard practice. The right questions to ask before any task, as Dr. Maura Grossman of the University of Waterloo, described it, are: “What are you trying to accomplish? And what’s the best tool for that?”

And verification is non-negotiable. AI tools can confuse a dissent with a majority holding, misstate what a lower court decided, or misquote language.

One simple rule, offered by Justice Kevin summed it up: “Whatever it gives me, I have to verify.” Every case that AI identifies, every citation it provides, every factual summary it generates must be checked against primary sources.

This is not a burden that undermines the value of AI; indeed, it is the practice that makes AI valuable.

What AI cannot do

In AI use in courts, the boundaries matters as much as the capability of the tools.

“This is not a truth-finding tool,鈥� explained Chief Justice Fader. 鈥淭his is a truth-agnostic, predictive tool.” AI generates statistically likely output based on patterns in its training data. It does not reason, weigh competing values, or sense that something in a case doesn’t add up. It has no judgment, no empathy, no moral compass, and no capacity to recognize when the law should evolve because justice requires it.

Judicial discretion is not an inefficiency AI can optimize away 鈥� and this is the point. As Justice Kevins said in the webinar, AI is “only an assistant, an extra assistant.” The analysis of how the law applies to a specific set of facts, before specific parties, in a specific moment 鈥� that is unavoidably human work. The moment any judge asks AI to assess which party has the stronger argument, a vital task has been delegated to the AI that should not have been.

Leading from the bench

Judges who engage with AI carefully set a visible standard for everyone who appears before them. These judges are better equipped to recognize when a filing鈥檚 AI-generated content is inaccurate. They can set informed expectations for clerks and staff, and they can ask the right questions when AI-related issues surface in litigation 鈥� many of which are already surfacing regularly.

The goal for judges is to use AI 鈥渢o do their work more productively, but not to replace their judgment,鈥� said Chief Justice Fader. And that balance should guide responsible judicial use: Neither avoidance, which is increasingly unrealistic; nor uncritical adoption, which carries real risk 鈥� but instead a more disciplined approach than either of these extremes.

The prepared judge is not simply the one who has read everything before walking into a courtroom. It’s the one who has used every available tool wisely, verified rigorously, and exercised the human judgment that no algorithm can replicate. That standard, consistently applied, is a form of leadership for which the legal profession has been waiting.

For more on the impact of AI in courts, visit the听

Key insights:

• • • The history of discovery is a history of matching tools to tasks 鈥� From paper review to keyword search to machine learning to generative AI, each innovation succeeded by assigning the right level of capability to the right kind of problem.

    • Adoption has outpaced understanding 鈥� AI discovery tools are now widely used, but the statistical literacy needed to defend their use in court has not kept pace.

    • Courts reward preparation, not adoption 鈥� Judges generally don’t weigh in on which tool a law firm uses; rather, they weigh in when counsel cannot explain or defend that choice.

Document review has always been organized around a basic question: Who, or what, is best suited to handle a given volume and type of material? In the paper era, that question was answered with people. Large cases were staffed with tiers of reviewers, working through boxes of documents. The model was labor-intensive but logical. More documents required more reviewers, and the work was distributed according to experience level, with junior reviewers handling first-pass relevance calls and more experienced attorneys handling privilege determinations and quality control.

The widespread shift to electronic discovery in the early 2000s introduced keyword search as an intermediate tool. Search terms allowed reviewers to narrow enormous document populations before human eyes even glanced at them, but the approach had limits as well. A search for “ice cream”, for example, would miss a document that said “gelato,” and a poorly constructed term list could either bury reviewers in false positives or miss documents entirely.

The next major shift came with the introduction of supervised machine learning in the late 2000s and early 2010s. This was a different kind of tool entirely. Rather than searching for fixed terms, the system learned from examples. A reviewer would mark documents as relevant or not relevant, and the system would apply that pattern across the rest of the collection, prioritizing documents most likely to matter. This was the first point at which legal discovery began to resemble a partnership between human judgment and statistical inference, rather than a purely manual or purely mechanical process.

Bringing GenAI in

The most recent shift, beginning around 2022, introduced large language models and generative AI (GenAI) into the same workflow. These tools do not simply sort or rank documents 鈥� they can summarize them, answer questions about them in natural language, and construct chronologies or timelines across a collection.

Each of these innovations did not in fact replace the one before it so much as they added a new layer of capability. Indeed, the central challenge in every era has been the same: The important determination is knowing which tool, or which level of human or machine capability, is appropriate for a given task.

For more on this, check out , featuring Dr. Maura Grossman

This is where the analogy to legal staffing becomes useful. A first-year associate and a senior attorney are not interchangeable, because they are suited to different kinds of judgment calls. The same logic increasingly applies to AI tools. A general-purpose AI model is not the same as a fiduciary-grade system built and validated for a specific legal task. Choosing a narrowly designed, purpose-built tool over a general one is the AI equivalent of choosing an senior attorney over a law school graduate for a task that requires accountability and demonstrated reliability, not just general competence.

The literacy gap that follows adoption

As these tools have become more capable, the legal profession’s ability to evaluate them has not necessarily kept pace. Evaluating AI discovery tools, both before they are adopted and after they generate output, requires such as recall, precision, confidence intervals, and margin of error. In fact, these are not concepts most lawyers were trained to work with, and many rely heavily on vendors to supply and interpret these metrics rather than developing the capacity to do so themselves. The risk is a quiet one: figures or citations that should raise concern can go unexamined simply because the people reviewing them do not know what to look for.

This gap matters because it shapes how discovery disputes are litigated. If one party argues that another’s recall rate is too low 鈥� meaning that a significant share of relevant documents was not produced 鈥� both sides need a working understanding of how that figure was calculated and what it means in order to argue the point credibly. The legal and technical questions in such disputes cannot be fully separated.

What courts actually expect

Discovery is , with judges generally staying out of methodology decisions entirely. Judicial involvement in AI-driven discovery is therefore a signal, not a routine occurrence. In fact, courts usually step in under two circumstances: i) a dispute over whether a chosen method is adequate; or ii) a timing problem severe enough to threaten the case schedule. Both situations typically indicate that something has already broken down. A low recall rate that prompts a motion or a delay serious enough to draw judicial questions, for example, suggests a deeper flaw in how the review was designed or executed and is sometimes serious enough that it can call an entire production into question rather than just the documents in dispute.

The common thread in these failures is governance, not technology. A capable AI tool used without a clear validation process, defined oversight roles, or documented standards for measuring output is no more reliable than an undertrained reviewer left unsupervised. Most of the disputes that escalate to a judge can often be traced back to a preventable gap: whether no one verified the tool’s metrics, no one understood what the numbers meant, or no one assigned responsibility for catching errors before production.

Those law firms that build proper AI governance into their discovery process, have clear protocols, defined accountability, and have a working understanding of how their tools are validated, are far less likely to need a judge to resolve what should have been caught internally.

The history of document review, at its core, is a history of matching capability to task, whether that capability comes from a person or a machine. As AI tools take on more of the discovery process, the skill that increasingly distinguishes effective use from risky exposure is not technical operation, but the ability to select the right tool for the task, understand what its outputs mean, and recognize those instances in which human review remains essential.

To learn more about how courts should approach AI and other advanced technology, check out the 成人VR视频 Institute’s

Key insights:

• • • Unauthorized practice of law doctrine is a poor fit for regulating AI and legal technology 鈥� UPL remains important when people represent others in court or affect legal proceedings, but it does not translate cleanly to software. Extending UPL to software moves the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm.

    • Legal technology should be regulated through consumer protection principles, not tool-based restrictions 鈥� Rather than asking whether an AI product or software platform “practices law,” regulators should focus on whether the tool misleads users, performs as advertised, provides adequate transparency, or causes demonstrable consumer harm.

    • A harm-based framework can protect consumers while expanding access to justice 鈥� Because most legal needs remain unmet, AI and justice tech can provide meaningful support to people who would otherwise receive no legal help. Safe harbors, disclosures, and accountability for fraud, negligence, or false advertising would better balance innovation with consumer protection.

This is the second of a two-part blog series examining how regulators, the legal profession, and individual litigants are looking at the unauthorized practice of law (UPL). We first looked at the history of UPL, and now this installment suggests a consumer protection-based method of regulation to replace today鈥檚 supplier-based regulations.

The legal industry has reached an inflection point in which the unauthorized practice of law (UPL) doctrine is no longer the right framework for regulating modern legal services. As technology reshapes how legal help is delivered, the focus must shift from policing who can provide legal support to ensuring that consumers are protected from harm.

This does not mean dismantling the doctrine entirely. UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Those considerations, however, do not translate cleanly to today鈥檚 AI-driven advanced technology. Extending UPL to software may shift the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm. A more effective approach is to apply existing consumer protection principles 鈥� such as fraud, negligence, and false advertising 鈥� to evaluate whether legal technologies are delivering accurate, transparent, and reliable support for users.

UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Over the past several decades, we have bent UPL law to fit emerging models of legal assistance, but with AI we have reached the point at which we should not attempt to stretch it further. AI has expanded the capabilities of legal technology beyond document automation to include research, summarization, and guided analysis. These tools are now widely available and are being used by individuals who would otherwise navigate legal issues without assistance.

At the same time, familiar regulatory arguments are being applied to these new tools. Assertions that such systems constitute UPL rely on assumptions that are increasingly difficult to reconcile with how these technologies function and are used.

Users generally understand that these systems are not lawyers. Engagement with AI tools is driven by accessibility, cost, and convenience, particularly in a landscape in which a substantial portion of legal needs go unmet. The more relevant inquiry is whether these tools provide a meaningful improvement compared to having no assistance at all.

Shifting from UPL to consumer protection

When consumers turn to technology, outcomes will vary, as they do across all forms of legal support. When issues arise, existing legal frameworks already provide mechanisms for accountability. Questions of fraud, negligence, and misleading representations can be addressed through established consumer protection laws without relying on an expanded interpretation of UPL.

Focusing on demonstrable harm rather than the mere existence of a tool aligns regulatory efforts with their intended purpose. It also reduces the risk of limiting innovation that could address persistent gaps in access to legal services encountered by so many individuals today. When liability is tied to the mere provision of technology, rather than to harmful conduct, the result is often reduced investment and slower development of potentially beneficial solutions.

Some jurisdictions have begun to adopt approaches that reflect this distinction. Non-prosecution policies, disclosure requirements, and clearly defined safe harbors for AI-driven tools can provide a framework in which innovation can proceed alongside appropriate safeguards. Indeed, these models emphasize transparency and consumer awareness while allowing for continued experimentation and improvement.

Legal doctrine in transition

Whether state regulators drive the change or watch from the sidelines, there are broader legal considerations on the horizon. On the federal level, the U.S. Federal Trade Commission (FTC) has already signaled that a change towards harm-based regulation as opposed to tool-centered regulation is coming.

In his concurrence in , which had billed itself to consumers as 鈥渢he world鈥檚 first robot lawyer,鈥� :

鈥淢y vote should not be taken as support for the State Bar of California鈥檚 claim that DoNotPay was engaged in the unauthorized practice of law. The Commission does not enforce state occupational-licensing laws like California鈥檚 unauthorized-practice-of-law prohibition. And if a company were to create a computer system capable of giving accurate legal advice and drafting effective legal documents, or honestly advertise a system that provides something less, I doubt that the aggressive enforcement of lawyers鈥� monopoly on legal service would serve the public interest.鈥�

This aligns with remarks from U.S. Supreme Court Justice Neil Gorsuch鈥檚 confirmation hearing when he stated: 鈥淲hy is it that every time certain companies that provide online legal services for basic things get sued every time they move into a new State?鈥�

While approaching it from different angles, courts are arriving at similar positions. In , an Oregon court of appeals case about fabricated citations, the court stated: 鈥淩egardless of provider, a generative artificial intelligence program is not, itself, a lawyer.鈥� While perhaps not intended, the logical extension of this is that a tool cannot be engaged in UPL 鈥� only the human using it can.

Finally, recent judicial reasoning, such as in the Supreme Court case of , suggests a closer examination of attempts to regulate speech by categorizing it as professional activity. The Court found professional speech protected by an 8-1 vote, suggesting bipartisan questions about whether professional licensing can continue to restrict speech.

Aligning regulation with reality

Within this evolving landscape, the central policy question is how best to protect consumers while enabling meaningful improvements in access to legal support. Frameworks designed for earlier models of service delivery can be difficult to apply effectively to new forms of technology without producing unintended consequences.

Instead, a consumer-focused approach can direct attention to the quality, accuracy, and transparency of services. It can evaluate whether users are misled, whether tools perform as described, and whether harm can be identified and addressed.

While AI-driven technology offers a way to extend the reach of legal support systems and to develop solutions that operate at a scale not previously achievable, the concept of UPL is still a valid one, albeit within a more limited and clearly defined scope.

As the legal system rapidly evolves, aligning regulatory approaches with current realities allows for both the protection of consumers and the development of the tools that expand access to justice in practical and sustainable ways.

You can find more about the challenges around issues of access justice here

Key insights:

• • • Agentic AI acts autonomously, creating new accountability challenges 鈥� Agentic AI acts and makes decisions with minimal human intervention, and this shift changes everything about responsibility and oversight.

• • • With intentional design, the risks can be addressed confidently 鈥� Silent failures, accountability diffusion, and confidentiality breaches can only be mitigated through governance, testing, and rigorous human oversight.

• • • AI is changing legal work, not eliminating it 鈥� When agentic AI handles routine tasks, legal professionals can move their attention onto higher-value work and increased responsibilities.

It is no longer useful to treat all AI as a single category or a tool for a single use case. Generative AI (GenAI) has already begun reshaping legal work by drafting documents, researching precedents, and answering questions with remarkable speed. At its core, however, GenAI remains a responsive tool. Agentic AI, on the other hand, represents a distinct evolution. Rather than waiting for a prompt, agentic AI systems can plan workflows, carry them out autonomously, and make decisions along the way.

As technology and the judicial system become increasingly intertwined, it is essential to examine where these more advanced tools intersect and what that convergence means for legal institutions. Ankita Upadhyay, Senior Director of AI Enablement at 成人VR视频, recently shared her perspective during a recent webinar,听, presented by the听听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥斕齛nd offered valuable insight into the opportunities and responsibilities that accompany this shift.

One of the key notes to understand from the panel is that 鈥済enerative AI gives you an answer. Agentic AI takes an action 鈥� and that distinction changes everything about the accountability,” Upadhyay said, adding that the distinction is not merely technical. It must reshape how we think about professional responsibility and the integration of AI into institutions that are built on trust and accuracy.

The promise of efficiency and transformation at scale

The potential of agentic AI is already visible in courts across the country. In Palm Beach County, Fla., for example, court officials are using agentic AI to process incoming documents at unprecedented scale. When an attorney files a document, the system autonomously identifies the document type, classifies it, extracts data, and routes it appropriately. The county already has processed up to 5 million documents using this process, operating 20 hours a day, every day of the year.

The most important part isn’t just the volume, however, it’s what happened to the people.

The staff who spent time on routine document processing were not laid off; instead, they were reassigned. Clerk 1 positions were transitioned to Clerk 3 and Clerk 4 roles, and that meant greater responsibility, more complex decision-making, and increased compensation for those making that transition.

“The staff that was doing all the processing of documents has been reallocated to customer experience and more complex tasks,” explained Parik Chokski, Director of IT for Palm Beach, on the webinar. This development reflects a broader truth: AI is not taking jobs in the legal sector; rather, it鈥檚 changing what those jobs entail.

You can explore the white paper听听here

As more repetitive work moves to AI, legal professionals move their attention toward the kind of work that demands their judgment, expertise, and accountability.

Risks are real, but not insurmountable

Yet the promise of agentic AI comes with genuine risks that differ from those posed by generative AI. Because agentic AI acts autonomously, for instance, failures can occur silently and invisibly, and sometimes repeatedly before detection.

成人VR视频鈥� Upadhyay identified three predominant risks for legal professionals and their organizations with agentic AI use:

1. Accountability diffusion 鈥� When an agentic AI system produces a document through a chain of autonomous decisions, it becomes difficult to determine where human judgment ended, and machine decision-making began. This ambiguity directly challenges professional conduct rules, which assume lawyers make every material decision. The result is an unclear line of responsibility and potential legal exposure for the lawyer.

2. Confidentiality at scale 鈥� Agentic AI systems operate across entire databases and multiple use cases simultaneously. A single misconfiguration of permissions can allow an AI agent to access privileged information to which it shouldn’t have access, potentially sharing sensitive client data across unintended matters. The danger lies in the fact that this often happens silently and repeatedly until discovered.

3. Irreversibility 鈥� Unlike GenAI, where a flawed draft often gets caught during review, agentic AI can send client communications, file documents, or update records based on faulty reasoning even before human oversight intervenes. The speed of action outpaces the speed of review, and thus, it creates a gap that traditional legal processes weren’t designed to address.

“The risk isn’t that AI gets it wrong,鈥� Upadhyay said. 鈥淭he problem is agentic AI systems, when it gets things wrong, it happens silently in a black box until you monitor it, and that’s the biggest challenge.”

Guardrails for responsible implementation

Given this, how do courts and legal organizations implement agentic AI thoughtfully? The webinar panelists, drawing on real-world implementations and NCSC research, emphasized several critical actions, including:

Establish clear governance 鈥� Begin with centralized registration of all agentic AI agents, conduct rigorous risk classification based on task impact, and start with low-risk workflows before advancing to high-stakes tasks. “Having a proper agentic AI governance is really important,” Palm Beach’s Chokski said.

Commit to rigorous testing 鈥� Extensive stress-testing in development and Q&A environments must precede any production deployment. Palm Beach’s implementation required weeks, if not months, of testing before going live 鈥� but that investment paid dividends in reliability and organizational confidence.

Design for transparency 鈥� Build workflows with built-in checks, balances, and fail-safes. Establish comprehensive logging that tracks what the AI agent does, what permissions it has, and what decisions it makes at each step. Monitor continuously for behavioral drift.

Maintain human oversight 鈥�”Trust but verify,” Chokski noted. Agentic AI is powerful and here to stay; but so are human professionals, and they must always retain oversight, the ability to intervene, and ultimate accountability for outcomes.

The conversation continues

The choice legal organizations face today is not whether agentic AI will exist, but how to engage with it responsibly.

Organizations that approach agentic AI with intentionality, clear frameworks, and commitment to human judgment will unlock its potential to expand capability, improve efficiency, and free legal professionals to do work that requires their expertise and accountability. Those that rush forward without guardrails risk silent failures that could undermine trust in both the technology and in the overall institution.

The path forward demands partnership: AI handles scale and speed, while humans provide judgment, accountability, and ethical reasoning. When those work two parts work together intentionally and with clear guardrails, that’s where justice is served.

For more on the impact of AI in courts, visit the听

Key insights:

• • • Knowledge flows both ways 鈥� The AI era has introduced a new dynamic in courthouses with knowledge flowing in more than one direction.

    • Both sides bring irreplaceable expertise鈥� Clerks bring AI fluency; and judges bring the legal instinct to know when something is wrong. Neither works without the other.

    • Informal learning must become institutional practice鈥� Courts that formalize this exchange, rather than leaving it to chance, will be better positioned for the wave of AI change ahead.

Picture a seasoned federal judge, with decades of experience on the bench and thousands of cases behind them, leaning in to watch a first-year law clerk navigate an AI research tool. The clerk is the one doing the teaching.

This scene, quietly playing out in courthouses across the country, may be one of the most under-reported shifts in how the judiciary is adapting to a rapidly changing technological landscape. On the latest episode of , Kaitlyn Frank, who leads courts-focused market intelligence for 成人VR视频, described exactly this phenomenon and what it signals for the future of judicial work.

From skeptics to early adopters 鈥� in 18 months

The pace of change has been striking. Courts and judges were among the least willing professional groups to adopt AI tools just 12 to 18 months ago. Today, research from the indicates that more than 60% of federal judges are using at least one AI tool in their work. That is not a gradual drift 鈥� it鈥檚 a shift.

What drove it? Judges recognized that the question was never really whether AI would enter the courthouse, but how well-prepared the institution would be when it arrived. Courts continue to experience significant staffing shortages 鈥� last year more than two-thirds (68%) of courts were facing staffing challenges, with nearly half of court professionals saying they lacked the time to complete their work, according to the . Yet, many court professionals say they have found particular value in tools that can assist with research, document summarization, and administrative workflows. This makes the clear point that AI, used well, does not replace the people doing this work, rather, it gives them room to do it better.

A symbiotic relationship at the heart of adoption

The traditional judge-clerk relationship is one of the most distinctive in the legal profession. Judges impart legal reasoning, institutional knowledge, and decades of pattern recognition. Clerks bring fresh legal training, intellectual rigor, and an outsider’s perspective. It has always been a two-way exchange, even when it was rarely described that way.

AI has made that dynamic more explicit and expanded it. Clerks arriving from law schools where AI literacy is increasingly woven into the curriculum bring a technological fluency that many sitting judges have not had the opportunity to develop. Judges, in turn, bring something AI cannot replicate: The hard-earned ability to sense when a legal argument is wrong before they can fully articulate why. One without the other creates risk; yet together, they form a genuinely effective check on AI output.

This symbiosis extends beyond individual courtrooms. Courts strengthen their institutional resilience when experienced staff and new clerks learn from each other, sharing knowledge about AI tools and their limitations. When the next generation of AI capabilities arrives 鈥� and it will 鈥� those courts will adapt from a position of strength rather than scrambling from a standing start.

Building the institutional foundation

AI in courts is developing rapidly, and that pace is largely a good thing. Legal research that once consumed hours can be completed in minutes; voluminous case files can be summarized with accuracy; and unfamiliar bodies of law can be mapped quickly, giving judicial staff a workable orientation before they dive deeper. These are meaningful gains for institutions that work under real resource pressure.

Those courts that are making the most of AI are not simply those with the most technologically curious judges or the most skilled clerks. They are the ones treating AI adoption as an institutional decision rather than an individual one. That means creating written policies that define appropriate use, establishing risk tiers that help staff calibrate how much oversight different tasks require, and developing training that evolves as the tools themselves evolve.

Some courts currently have no official AI policy in place, creating a risk that staff may use AI inappropriately. And without a shared framework, every individual is left to make judgment calls that should be made collectively 鈥� and the knowledge being built through experimentation stays siloed rather than becoming a shared institutional asset.

Courts that invest in policy infrastructure now are not slowing themselves down. They are building the foundation on which broader, more confident AI adoption becomes possible.

A new kind of judicial wisdom

The best judges have always learned from the people and experiences around them 鈥� through difficult cases, insightful clerks, and fellow judges. A willingness to keep learning throughout their careers is itself a mark of judicial wisdom.

AI does not change that instinct; rather it expands the range of what there is to learn from, and who is doing the teaching.

For courts navigating this moment, the reverse mentorship dynamic offers more than a practical model for AI adoption. It offers a reminder that the judiciary has always adapted by drawing on the full range of knowledge within its walls. The same should be true now, because even though the tools are newer, the principle is not.

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Key insights:

• • • Unauthorized practice of law rules have repeatedly come into conflict with new forms of legal self-help 鈥� Each major wave of consumer-facing legal assistance has tested the boundaries of UPL doctrine and forced courts, regulators, and lawmakers to decide where legal information ends and legal advice begins.

    • Technology has expanded access to legal information faster than regulation has adapted 鈥� LegalZoom and other justice tech companies showed that legal tools could be delivered at scale, while UPL doctrine often struggled to accommodate new models of legal assistance designed for consumers with unmet legal needs.

    • The rise of AI makes the old UPL framework increasingly inadequate 鈥� As GenAI tools provide legal research, document assistance, and guided analysis directly to the public, regulators should move beyond the LegalZoom-era battles and consider a framework focused on consumer protection, transparency, and actual harm.

This two-part blog series examining how regulators, the legal profession, and individual litigants are looking at the unauthorized practice of law (UPL) first looks at the history of UPL and then suggests a consumer protection-based method of regulation to replace today鈥檚 supplier-based regulations.

With three-quarters of state court cases including at least one self-represented party, and with 92% of Americans with a legal problem not getting the legal help they need, it鈥檚 not surprising that the unauthorized practice of law (UPL) is a concept that鈥檚 not far from people鈥檚 minds.

It does not have to be this way, of course, and there are solutions to the thornier issues with UPL; but first, it may be helpful to understand how we got to this place and how UPL has evolved.

Legal self-help in a pre-Internet world

In the late-1800s, before UPL was formally articulated, John Wells published “Every Man His Own Lawyer”, a widely circulated guide that explained legal principles and provided practical forms. Its popularity reflected sustained public demand for accessible legal information. Around the same time, the organized bar began to emerge, along with more structured efforts to define and protect the boundaries of legal practice.

By the early-1900s, auto clubs were providing legal help to their members, demonstrating an early form of a prepaid legal services plan that exists to this day, but with typically a wider array of services. As would be the case in later years, an economic downturn soon brought a fight as lawyers used threats of UPL to fight competition. Not long after the Great Depression began, the ABA formed the Committee on Unauthorized Practice of Law, and a wave of litigation ensued to essential end the offering from auto clubs.

Similar dynamics appeared later in the 20th century. In the 1960s, soon before the recession of the 1970s, Norman Dacey鈥檚 “How to Avoid Probate!” offered readers tools to manage estate planning without engaging a lawyer. The response included investigations and attempts to suppress the book. Courts ultimately clarified that providing general legal information, even when presented in a structured and practical format, does not constitute individualized legal advice and falls within the scope of protected speech.

Tech enters the equation

By the 1990s, these ideas had moved into a digital environment. Companies such as Nolo and Parsons Technology translated legal forms and guidance into software and the Texas State Bar sued in federal court. Although the bar initially prevailed, a legislative response introduced a software exception to UPL that remains in effect today, reflecting an early acknowledgment that technology-based tools required a different regulatory lens.

By early 2000s, LegalZoom extended these concepts at scale. By automating document creation across a wide range of legal needs, it brought structured legal tools directly to consumers in a more accessible format. While not the first provider of self-help legal resources, it demonstrated how technology could move online and operationalize these services at a national level 鈥� not surprisingly, this effort would face resistance at a whole new level.

Launched in 2001, LegalZoom argued that it just represented the modern evolution of books like those written by Wells and Dacey. The response from the legal establishment was ferocious. It began with state bar inquiries trying to understand what LegalZoom was offering, and as the Global Financial Crisis began in 2007, class action lawsuits and regulatory challenges followed.

These suits sought significant damages without alleging specific consumer harm, creating substantial pressure on a still-developing sector and signaled resistance to new models of service delivery. The objections were ostensibly about consumer protection, while more reflecting concerns about changes to established structures in the legal profession.

LegalZoom won some of the class actions and settled others on friendly terms, typically agreeing to limit the use of certain words in its advertising, paying some class member claims, offering its attorney-access plans on a complimentary basis, and paying attorneys鈥� fees.

Supreme Court precedents

Two U.S. Supreme Court decisions would prove highly important to the UPL battles. The first came in in which the Court ruled that companies could include class action waivers in arbitration provisions. Soon after, LegalZoom began implementing this type of arbitration provision to coincide with the resolution of several major class actions to make sustaining a class action against it in the future more difficult.

The second Supreme Court ruling to impact UPL came in in which the Court ruled that a state occupational licensing board cannot claim state-action antitrust immunity if a controlling number of its decision-makers are active market participants in the occupation it regulates and the state does not actively supervise the board. This decision put state bars at risk.

The fight that changed the conversation was the LegalZoom lawsuit against the North Carolina State Bar (NCSB) that was modeled after the result in the Dental Board matter. LegalZoom had built a prepaid legal services plan offering attorney access to its customers 鈥� a narrower version of what the auto clubs had offered in the past. These types of plans historically were supported by the ABA and National Association of Attorneys General, but a few states pushed back on LegalZoom offering one. Most notably, North Carolina objected and LegalZoom sued the NCSB for a declaratory judgment that it was not engaged in UPL as well as on antitrust and other grounds, leading to a settlement and cooperative legislation that cleared the way for LegalZoom to continue operations, including launching its legal plan, in that state.

Upon the case’s conclusion, University of Tennessee College of Law professor , LegalZoom fought the North Carolina Bar 鈥� and LegalZoom won. Barton opined that the 鈥淪outh Carolina [where the Supreme Court had found LegalZoom practices lawful] and North Carolina precedents will likely end all state bar action on UPL.鈥� He was largely correct, as future LegalZoom and other industry skirmishes would not amount to much, allowing the industry to thrive.

The future of UPL

Today, the LegalZoom fights look quaint. It was essentially a fight over the online equivalents to form books, when a few years later AI would explode onto the scene and upend everything. We now have everything from foundation models such as ChatGPT, Claude, and Gemini to legal specialists available to the public and generating research memos at the push of a button.

This, perhaps, brings us back to where we started. And now may be the time to ask whether a new system of regulation is needed around UPL, because no other justice tech company should have to run the gauntlet of fights that LegalZoom faced.

In the next part of this blog series, we will look at how the issues raised by UPL in the AI age may require a new regulatory solution, possibly one based on a consumer protection model that would replace today鈥檚 supplier-based regulations

Key insights:

• • • Accountability, not adoption, is the central governance challenge听鈥� With many institutions using AI a variety of tasks, informal “shadow AI” use is expanding without consistent oversight.

    • Justice systems now face a parallel governance problem 鈥� They must find a way to regulate AI while using AI inside the institutions that enforce rights, while allowing responsible innovation that improves efficiency and access to justice.

    • AI needs to be integrated into broader justice reform听鈥� Without strong data governance and clear boundaries between AI assistance and legal judgment, courts risk automating inefficiency, deepening inequities, and undermining public trust.

Even as AI governance frameworks remain mired in ongoing debate, justice systems are moving ahead with implementation. Courts and dispute resolution institutions are integrating AI into their operations to more efficiently digitize records and automate workflows.

This introduces the very real challenge of parallel governance. We must now determine not only how AI should be regulated, but how it operates within the very institutions responsible for enforcing rights.

And this intersection is no longer theoretical: Does AI governance strengthen fairness, preserve independence, and expand access 鈥� or does it undermine their very foundations?

From experimentation to embedded use

Across jurisdictions, AI is often framed as an administrative tool that can handle basic tasks such as transcription, translation, case triage, and more, as well as providing analytics to identify delays or inefficiencies.

These applications respond to real constraints, such as overburdened courts, limited resources, and persistent backlogs. Similarly, dispute resolution platforms are integrating AI to guide users through processes and structure negotiations.

However, this formal adoption tells only part of the story. AI is also entering justice systems informally. Judges, clerks, and lawyers are independently using general-purpose tools in their daily work, often without guidance, oversight, or a clear grasp of the tools鈥� implications for security, confidentiality, and discoverability. As one expert observed: 鈥淪hadow AI is already happening.鈥�

The absence of governance does not prevent AI use; and, in fact, it may encourage misuse. This shadow AI simply pushes AI usage into unstructured and unmonitored areas 鈥� the risk then becomes not adoption itself, but uneven adoption that evolves beyond institutional control.

It鈥檚 no longer a question that justice systems need to engage with AI; however, that engagement has be done deliberately and in a way that allows governance frameworks to keep pace without constraining beneficial use.

While it鈥檚 no longer a question that justice systems need to engage with AI, that engagement has be done deliberately and in a way that allows governance frameworks to keep pace without constraining beneficial use.

Automating inefficiency?

Efficiency is often the entry point for AI in justice systems; but efficiency alone is not reform. And misapplied efficiency can often lead to its direct opposite: a scramble to repair broken systems or to plug technology and personnel gaps.

Many current AI initiatives remain isolated pilots 鈥� layered onto existing processes rather than integrated into broader institutional strategy. Without addressing underlying structural constraints like fragmented data, inconsistent procedures, and uneven infrastructure, AI risks automating inefficiency rather than resolving it. And without strong data governance, infrastructure, and institutional alignment, even well-designed AI tools will underperform or produce unreliable outcomes.

That means that efforts to tightly control AI deployment without addressing these foundational issues risk focusing on symptoms rather than the system itself. AI should not function as a parallel modernization effort; rather, it must align with broader justice system reform.

Clearly, the most consequential questions arise when AI tools begin to shape legal reasoning or outcomes. And while there is broad agreement that AI can support judicial work without replacing independent human judgment, in practice, however, the boundary between assistance and influence is not always clear.

Even administrative tools can shape decisions. Summaries may omit nuance, or suggested language can influence framing. Over time, reliance on system outputs can create subtle forms of dependency. In fact, this dynamic is compounded by what has been described as the myth of verification 鈥� the assumption that human oversight alone is sufficient. In reality, time constraints, cognitive bias, and limited technical fluency can make meaningful review difficult. And automation bias affects even experienced decision-makers.

Overall, these boundaries require deliberate definition. Left on their own, AI tools and their outputs will be shaped implicitly through practice rather than through principled governance.

Design determines outcome

Institutional capacity will determine how these dynamics play out because digital maturity varies widely across jurisdictions. Some courts operate advanced platforms, while others remain largely paper based. In lower-resource environments, infrastructure may not support even basic digitization. In more advanced systems, adoption may outpace governance.

Yet, one consistent challenge among all jurisdictions is reliance on external vendors. Without internal expertise, institutions risk adopting tools that meet technical requirements but fall short of rule-of-law standards, particularly in transparency, accountability, and data governance.

Justice systems are not neutral environments for technology adoption 鈥� they are the operational core of the rule of law.

Addressing this gap requires more than a procurement issue. It requires institutional literacy. Judges and administrators need a working understanding of how AI systems function, where risks arise, and how to evaluate them. Training efforts are underway, but scaling this capacity will take time. In the interim, governance gaps will persist and attempts to compensate for these gaps through overly rigid restrictions may limit adoption but do little to build the institutional capability required for effective oversight.

From adoption to accountability

Clearly, AI will not improve justice systems by default; rather its impact will be determined by institutional design, which includes clear boundaries on use, transparency around deployment, safeguards to protect independence, and mechanisms for oversight and accountability. It also requires alignment with broader justice system goals of efficiency, fairness, and accessibility.

Yet, justice systems are not neutral environments for technology adoption. They are the operational core of the rule of law. Their legitimacy depends on trust, which in turn requires accountability.

This makes the path forward not purely a technical one. It requires institutional self-assessment, alignment with human rights frameworks, and collaboration across policymakers, courts, technologists, and the public. The measure of success will not be the sophistication of the tools deployed, but whether they strengthen the system鈥檚 core functions of impartiality, accessibility, and trust.

AI tools can support those goals, of course, but only if they are designed into justice systems from the outset.

You can find other installments of听our Scaling Justice blog series here

Key insights:

• • • AI as a design decision, not just a tech add-on 鈥� AI gives us a chance to rethink the 鈥渕achinery of justice鈥� and redesign it for today鈥檚 needs rather than simply automating existing systems and processes.

    • AI to expand access and usability, without replacing judgment 鈥� The most promising value is in reducing friction for litigants and helping people navigate the process.

    • Progress requires disciplined, court-by-court experimentation 鈥� We can start small, build AI literacy, set leadership tone, invite diverse perspectives, and address legal and ethical issues as design constraints, not deal-breakers.

Today, interest in AI across the judiciary is clearly growing, but most discussions are still constrained by certain fears:

• • • Fear that AI will replace human judgment 鈥� This concern is legitimate, but it focuses almost entirely on endpoints. Judging (and the systems around it) involve far more than final decisions. Focusing only on high-stakes endpoints misses much of what judges and courts do day-to-day.
    • Fear of hallucinations, errors, and bias 鈥� These are also legitimate fears, but there are ways to mitigate these risks, which are not new. The source may be different, but we have long needed to protect against errors, bias, and misstated law.
    • Fear of change 鈥� This is a difficult one to overcome, but a desire to protect the status quo sometimes presupposes that the system as it exists today is working exactly as it should. It isn鈥檛. At least not for everyone.

I鈥檇 like to see the narrative shift from fear of AI in courts, to the possibilities of AI in courts. AI presents a rare opportunity to upgrade the machinery of justice.

Justice as machinery

Most of us were taught to think about justice as an outcome, something the system delivers. However, justice is also the machinery we use to deliver it, and that machinery is a set of design choices. Rules, procedures, forms, hearings, briefs 鈥� we crafted these frameworks to manage conflict and produce decisions that feel fair and legitimate. Like most frameworks, they reflect the era in which they were built.

Once we start thinking about justice as something to be designed rather than simply delivered, the access-to-justice problem looks different. The question is no longer how to get more of the current system to more people; rather, it鈥檚 whether the machinery itself is still fit for its purpose.

Reimagining the machinery

The machinery has been redesigned before. Justice was once deeply human because it had to be: Law lived in minds, judges traveled from town to town, decisions were announced aloud. That system was more human and personal, but it was limited, exclusionary, and fickle. It was dependent on local norms and personal relationships. It yielded uneven outcomes.

The first great upgrade was writing, and more importantly, the printing press. It brought stability and protected litigants from arbitrary local power. But it also entrenched a new kind of authority. Yet, understanding it required literacy, training, and expertise. A professional bar emerged and ordinary people were pushed further from the center of their own disputes. Then came the digital age. It optimized the process and made more information available. But many people feel overwhelmed by the deluge of information and experience modern justice as a series of obstacles.

Does AI present a different kind of opportunity? Could it deliver an upgrade that finally closes the gap rather than widens it? I鈥檓 optimistic that the answer is yes, but our design choices matter and we have to be willing to reimagine justice from the ground up.

What if every litigant had access to an AI agent that could help them navigate forms, understand the process, and translate legalese? What if AI could take messy human stories and translate them into structured information for the court? What if courts offered AI-assisted dispute resolution in the early stages of litigation or at key milestones during the litigation? Can AI make navigating the legal system feel less like data entry and more like a conversation?

We鈥檙e not ready for giant leaps, and we can鈥檛 ignore the open questions: Unauthorized practice of law issues, privilege and work product implications, the reliability of AI-assisted work product, and more 鈥� but these are not dead ends. They鈥檙e current design constraints to account for, and they shouldn鈥檛 keep us from reimagining what鈥檚 possible.

Where do we start?

The institution of justice will not be redesigned overnight, and there is no central authority to drive change. Rather, it will be redesigned court by court. The principles below apply broadly and reflect a starting point for thinking about AI as a design decision, not just a technology decision.

Set the tone from the top听

Fear can be paralyzing, and in courts it often is. If judges and court staff are afraid to experiment, nothing moves. We need environments in which thoughtful, controlled experimentation is encouraged and supported. When more people are engaged in testing ideas and thinking about how to improve their processes, the likelihood of meaningful innovation and redesign increases.

Court leadership can create that space by setting a vision, encouraging responsible experimentation, and supporting innovative mindsets.

Build AI literacy

Encouraging experimentation is an important first step, but it can create risk if not paired with the right training and education. AI requires new competencies in prompting, guardrail development, output verification, bias awareness, iteration, context framing, documentation for audibility, fit-for-purpose judgment, and more. As tools evolve, education should evolve, too. Agentic AI, for example, will require a different set of skills and a different type of supervision than we鈥檙e accustomed to now.

For more information about toolkits and resources around AI in courts, visit

Judges and court staff do not need to become technologists, but they need enough training and education to ask the right questions, spot the right issues, and use the tools responsibly.

Rethink the systems, not just the tools

This one is critical. Currently, most conversations about AI focus on use cases, such as whether AI can assist with research or automate certain workflows. These are good questions, but the tougher questions will lead to bigger rewards. Where are our pain points? What can we do better? Which policies and processes are essential, and which have never been re-examined? Which parts of the machinery were built for a different era and have outlived their usefulness? And perhaps most importantly, who is the system failing?

We shouldn鈥檛 start with the technology and look for places to apply it. We should start with the people we serve and ask how the technology can help us serve them better.

Invite diverse perspectives

The strongest ideas emerge from the push and pull of different viewpoints. Court leadership can form committees that bring together innovators and skeptics, technologists and traditionalists, those who are excited and those who are concerned. We also need perspectives across different court functions. AI is not something to hand off to IT departments. They are essential partners, but the questions AI raises go far beyond any one department.

Outside perspectives are helpful, too. Many people across the country are already approaching this work with a multidisciplinary lens, and courts can draw on that experience.

Finally, remember to start small

It鈥檚 easy to create so much process and deliberation that progress slows. We need concrete steps that move us forward, however incrementally. Start with policies and data governance, then move to small, targeted pilots that can address low-hanging fruit. Small adjustments can help teams become comfortable with change; and early wins build confidence and create momentum.

Closing thoughts

Justice has been redesigned before, and it is on the brink of being redesigned again. AI will reshape courts whether or not we participate. However, as the people who know the system from the inside and want it to work for everyone, we may be in the best position to guide the next upgrade. The chance to build something more equitable, more accessible, and better designed for today鈥檚 world does not come around often, let鈥檚 not miss it.

You can find more insights from Judge Braswell here

Key insights:

• • • AI augments judicial judgment without replacing it 鈥� Used thoughtfully it clarifies reasoning and improves access.

    • Strict guardrails are needed 鈥� These can include structured prompts, anonymized data, and rule-based outputs helps interrupt bias and maintain integrity.

    • Judges should lead 鈥� They can do this through peer learning and education, which fosters responsible use while preserving public trust.

The integration of AI in the judiciary is gaining momentum, offering a promising solution to the growing caseloads, access-to-justice gaps, and public trust challenges faced by courts across the United States. And as the judiciary explores the potential of AI, a crucial conversation is emerging 鈥� one that highlights the importance of responsible and thoughtful adoption.

A recent webinar, , presented by the听 鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥� shed light on the experiences of early adopters of generative AI (GenAI) in the judiciary. In the webinar, Prof. Amy Cyphert of West Virginia University and U.S. Magistrate Judge Maritza Dominguez Braswell of the District of Colorado shared their insights from their own use of AI, emphasizing the need for a deliberate and informed approach.

The role of AI in judicial decision-making

A common fear is that AI will somehow take over the position of final arbiter in court proceedings. However, judges are not interested in having AI displace their judgment; rather, they see AI as a tool that augments and helps advance justice, not a tool that replaces decision-making or human judgment.

Judges also are not rushing into AI use. Instead, they are approaching it with a deep commitment to responsible use and a desire to increase, not decrease, public trust. “Everybody on that spectrum 鈥� from ‘I’m just learning’ to ‘I want to be a power user’ 鈥� says, ‘But I want to do it right,鈥�” says Judge Braswell.

AI can also help judges close communication gaps. By taking decisions that judges have already reasoned through and converting them into accessible explanations, AI can help all litigants clearly understand the relevant legal framework, rule, or process behind the decision. This is even more impactful in cases involving self-represented litigants.

Leveraging AI to enhance judicial communication

Judge Braswell understands this well. In every case with at least one self-represented litigant, she offers a plain language summary of her written decisions. Although she does not use AI to draft those, she does use AI to translate complex legal reasoning when delivering information from the bench.

鈥淚f I have 15 minutes for a hearing and want to explain to a self-represented litigant something complex, I use AI to help me translate legal jargon into plain and simple language,鈥� she explains. 鈥淚 want the self-represented litigant to understand what I鈥檓 doing and why I鈥檓 doing it 鈥� and AI helps me translate lawyer-speak into plain-speak, quickly.鈥�

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This capability is particularly valuable for judges who often struggle to find the time to connect with litigants. By leveraging AI, they can provide more personalized and informative interactions, ultimately enhancing litigants鈥� judicial experiences. In addition, some judges are using AI to create engaging content, such as avatars and videos on YouTube, to make themselves more relatable and accessible to the public; while others are using AI to help litigants navigate court processes, helping to demystify the system and reduce anxiety.

Guardrails for responsible AI use

Of course, Judge Braswell doesn’t use AI casually. She has strict policies and protocols in place, including segregation of work and personal accounts, prompt anonymization, and prohibiting her clerks from uploading sensitive information or delegating core functions and judgment to any AI tool. She also trains her chambers on high-risk and low-risk cases and emphasizes the importance of proper AI use through structured prompts, appropriate settings, standing instructions, and deliberate guardrails.

For example, Judge Braswell describes a dedicated project in which she uploaded her district’s local rules, the Federal Rules of Civil Procedure, and standing orders. She queries that project any time she needs to refresh on an applicable rule or procedure. She gave the AI tool clear instructions, such as: Don’t answer unless grounded in a rule. Cite the rule with every response. If you don’t know, say so.

While these types of practices do not make the tools risk-free, Judge Braswell notes, they do offer guardrails to help support, rather than undermine, judicial integrity.

Addressing risks and challenges

While , the deeper risks in AI use in the courts are bias, cognitive deskilling, and erosion of public trust. Judge Braswell warns that bias is harder to detect than any made-up case citation. “If you ask for a legal framework in an employment discrimination case, the system may pull more from defense-side articles because larger firms publish more content,鈥� she explains. 鈥淭he result is a subtle tilt in perspective.”

To counter this, she prompts her AI tools deliberately asking for diverse perspectives, asking the tool to gather contrary views, or telling the tool to answer only after asking follow-up questions that could identify user bias. Without this intentionality, bias can go undetected.

For judges ready to engage, visit听听to join the conversation

On the webinar, Prof. Cyphert echoed concerns about the next generation. “I worry that younger lawyers may skip critical learning processes if they rely too heavily on AI for drafting or research,” Prof. Cyphert says. “Is there a cognitive benefit to writing that we’re losing?”

The path forward through education, experimentation & transparency

During the webinar, both speakers rejected mandatory disclosure rules as counterproductive.

“It creates a chilling effect,” Judge Braswell says. 鈥淎nd we need people to engage for learning purposes.鈥� Instead, she notes that she advocates for voluntary transparency 鈥� judges explaining their use of AI in ways that build public understanding and confidence.

Prof. Cyphert agrees. 鈥淵ou can’t assess risks and benefits if you don’t understand the technology,鈥� she says, adding that she encourages judges to attend webinars, read research, and talk to peers. Similarly, Judge Braswell co-founded the , a judge-only, peer-led forum for candid discussion that exists as a safe space to share challenges, test ideas, and learn together.

As the webinar notes, the future of justice isn’t just about whether courts and judges are using advanced AI technology, it’s about how that technology should be used 鈥� with care, purpose, and always with people at the center.

For more on the impact of AI in courts, visit the听