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�

You can explore the white paper听 here

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

Key insights:

• • • Well-being is a professional necessity 鈥� Judges experience decision fatigue, emotional stress, and personal biases that can affect their rulings, making mental and physical well-being a judicial duty.

    • Community engagement builds better judgment 鈥� Staying connected to the communities they serve helps judges develop empathy, recognize bias, and deliver fairer decisions.

    • Diverse experience strengthens the judiciary 鈥� Varied backgrounds and ongoing education in areas like restorative justice make courts more responsive, inclusive, and publicly trusted.

Judges play a unique and essential role in society. They are tasked with interpreting the law, resolving disputes, and upholding justice 鈥� often under intense scrutiny and pressure. Their decisions shape lives, influence public policy, and reinforce the rule of law.

Indeed, judicial rulings may be the most visible part of the job, but they are not the only measure of a judge’s effectiveness 鈥� or of the judiciary’s overall health.

To truly understand and support a robust legal system, it is vital to look beyond the courtroom and examine the broader context in which judges operate. A judiciary that is fair, empathetic, and resilient depends not only on legal expertise, but also on balance, self-awareness, and active engagement with the communities it serves.

The weight of the robe & the value of connection

Despite the solemnity of the judicial office, judges also carry personal experiences, cognitive biases, and emotional responses. The weight of responsibility in adjudicating complex, often emotionally charged cases can lead to stress, burnout, and decision fatigue. that judicial decisions can be influenced by factors such as time of day, caseload volume, and even personal well-being.

When judges prioritize their own well-being through physical health, mental resilience, and time away from the bench, they are better equipped to render fair and consistent decisions. Judicial wellness is not a personal luxury; rather, it is a professional imperative.

Equally important is the role of community engagement. The law does not exist in a vacuum but is shaped by social norms, economic realities, and cultural shifts. Judges who remain isolated from the communities that are affected by their rulings risk losing touch with the lived experiences of the people before them.

Judicial rulings may be the most visible part of the job, but they are not the only measure of a judge’s effectiveness 鈥� or of the judiciary’s overall health.

Engagement with the public helps judges better understand how the law impacts and operates in people’s lives. It also builds the empathy and contextual awareness needed for interpreting statutes or imposing sentences.

For example, a judge who volunteers with youth programs or participates in community forums on public safety may develop a more nuanced understanding of cases involving juvenile offenders or policing practices. Similarly, a judge who attends local cultural events or listens to community leaders may be better positioned to recognize implicit biases or systemic inequities that may be inherent in the justice system.

Community involvement also strengthens public trust. When citizens see judges as accessible and engaged, rather than distant or aloof, confidence in the judiciary increases. And these ideas of transparency and connection are key to maintaining citizens鈥� trust in the courts.

These themes are explored more in depth in the 成人VR视频 Institute鈥檚 video series,听Beyond the Bench. For example, in the episode听,听Associate Justice Tanya R. Kennedy shares her experience educating youth, participating in civic organizations, and leading legal reform initiatives. The episode also highlights how service beyond judicial duties enhances judges鈥� decision-making and strengthens community ties.

Another episode of the series,听,听examines the personal and professional challenges faced by judges and attorneys alike. It features a candid interview with Judge Mark Pfiffer, who emphasizes the importance of mindfulness, peer support, and institutional policies that promote mental health and sustainable work practices.

A judiciary that reflects society

The same principle applies at the institutional level. A judiciary is strongest when it reflects the range of experiences and perspectives present in the society it serves.

Beyond individual judges, the judiciary can benefit from diversity and inclusion. A bench that reflects the full spectrum of society is more likely to deliver balanced and equitable justice. But diversity is not just about representation 鈥� it鈥檚 also about perspective.

Judges who have worked in public defense, civil rights advocacy, or rural legal services bring different insights to the bench than those who have spent their careers in corporate law or prosecution. These varied experiences enrich judicial deliberation and help ensure that decisions are informed by a broad understanding of justice.

Encouraging judges and court personnel to engage in lifelong learning, mentorship, and cross-sector collaboration further strengthens the judiciary. Programs that support judicial education on topics like implicit bias, trauma-informed practices, or restorative justice are essential to modern, responsive courts.

Improving judges鈥� well-being

The quality of justice depends not only on what happens in the courtroom, of course, but on what happens outside of it. Judges who maintain personal balance, engage with their communities, and remain open to diverse perspectives are better equipped to serve the public good.

Legal professionals, court administrators, and policymakers should support the kinds of initiatives that promote judicial wellness, community outreach, and professional development. By fostering a judiciary that looks beyond the bench, we ensure a justice system that is not only legally sound, but also humane, inclusive, and trusted.

In the end, judges and the justice they mete out are not defined by court rulings alone. It also depends on relationships, context, and public trust. Recognizing that reality is essential to preserving the well-being of the judiciary and the integrity of the law.

The听鈥�Beyond the Bench鈥澨齰ideo series is available on

Key takeaways:

• • • AI frameworks need to keep up with implementation 鈥� While AI governance frameworks are being developed and enacted globally, their effectiveness depends on enforceable mechanisms within domestic justice systems.

    • Access to justice is essential for trustworthy AI regulation 鈥� Rights and protections are only meaningful if individuals can understand, challenge, and seek remedies for AI-driven decisions. Without operational access, governance frameworks risk remaining theoretical.

    • People-centered justice and human rights must anchor AI governance 鈥� Embedding human rights standards and ensuring equal access to justice in AI regulation strengthens public trust, accountability, and the credibility of both public institutions and private companies.

AI governance is accelerating across global, national, and local levels. As public investment in AI infrastructure expands, new oversight bodies are emerging to assess safety, risk, and accountability. The global policy conversation has from principles to the implementation of meaningful guardrails and AI governance frameworks, which legislators now are drafting and enacting.

These developments reflect growing recognition that AI systems demand structured oversight and a shift from voluntary safeguards and standards to institutionalized governance. One critical dimension remains underdeveloped, however: how do these frameworks function in practice? Are they enforceable? Do they provide accountability? Do they ensure equal access?

AI governance will not succeed on the strength of international declarations or regulatory design alone; rather, domestic justice systems will determine whether it works. At this intersection, the connection between AI governance and access to justice becomes real.

In early February, leaders across government, the legal sector, international organizations, industry, and civil society convened for an expert discussion. The following reflections attempt to build on that dialogue and its urgency.

From principles to enforcement

Over the past decade, AI governance has evolved from hypothetical ethical guidelines to voluntary commitments, binding regulatory frameworks, and risk-based approaches. Due to these game-changing advancements, however, many past attempts to provide structure and governance have been quickly outpaced by technology and are insufficient without enforcement mechanisms. As Anoush Rima Tatevossian of The Future Society observed: 鈥淭he judicial community should have a role to play not only in shaping policies, but in how they are implemented.鈥�

Frameworks establish expectations, while courts and dispute resolution mechanisms interpret rules, test rights, evaluate harm, assign responsibility, and determine remedies. If individuals are not empowered to safeguard their rights and cannot access these mechanisms, governance frameworks remain theoretical or are casually ignored.

This challenge reflects a broader structural constraint. Even without AI, legal systems struggle to meet demand. In the United States alone, 92% of people do not receive the help they need in accessing their rights in the justice system. Introducing AI into this environment without strengthening access can risk widening, rather than narrowing, the justice gap.

There’s growing recognition that AI systems demand structured oversight and a shift from voluntary safeguards and standards to institutionalized governance.

Justice systems serve as the operational core of AI governance. By inserting the rule of law into unregulated areas, they provide the infrastructure that enables accountability by interpreting regulatory provisions in specific cases, assessing whether AI-related harms violate legal standards, allocating responsibility across public and private actors, and providing accessible pathways for redress.

These frameworks also generate critical feedback. Disputes involving AI systems expose gaps in transparency, fairness, and accountability. Legal professionals see where governance frameworks first break down in real-world conditions, often long before policymakers do. As a result, these frameworks function as an early signal of policy effectiveness and rights protections.

Importantly, AI governance does not require entirely new legal foundations. Human rights frameworks already provide standards for legality, non-discrimination, due process, and access to remedy, and these standards apply directly to AI-enabled decision-making. 鈥淎I can assist judges but must never replace human judgment, accountability, or due process,鈥� said Kate Fox Principi, Lead on the Administration of Justice at the United Nations (UN) Office of the High Commissioner for Human Rights (OHCHR), during the February panel.

Clearly, rights are only meaningful when individuals can exercise them 鈥� this constraint is not conceptual, it鈥檚 operational. Systems must be understandable, affordable, and responsive, and institutions should be capable of evaluating complex, technology-enabled disputes.

Trust, markets & accountability

Governance frameworks that do not account for these dynamics risk entrenching inequities rather than mitigating them. An individual鈥檚 ability to understand, challenge, and seek a remedy for automated decisions determines whether governance is credible. A people-centered justice approach, as established in the , asks whether individuals can meaningfully engage with the system, not just whether rules exist. For example, women face documented barriers to accessing justice in any jurisdiction. AI systems trained on biased data can replicate or amplify existing disparities in employment, financial services, healthcare, and criminal justice.

鈥淚nstitutional agreement rings hollow when billions of people experience governance as remote, technocratic, and unresponsive to their actual lives,鈥� said Alfredo Pizarro of the Permanent Mission of Costa Rica to the UN. 鈥淧eople-centered justice becomes essential.鈥�

AI systems already shape outcomes across employment, financial services, housing, and justice. Entrepreneurs, law schools, courts, and legal services organizations are already building AI-enabled tools that help people navigate legal processes and assert their rights more effectively. Governance design will determine whether these tools help spread access to justice and or introduce new barriers.

Private companies play a central role in developing and deploying AI systems. Their products shape economic and social outcomes at scale. For them, trust is not abstract; it is a success metric. 鈥淚nnovation depends on trust,鈥� explained Iain Levine, formerly of Meta鈥檚 Human Rights Policy Team. 鈥淲ithout trust, products will not be adopted.鈥� And trust, in turn, depends on enforceability and equal access to remedy.

AI governance will succeed or fail based on access

As Pizarro also noted, justice provides 鈥渘ormative continuity across technological rupture.鈥� Indeed, these principles already exist within international human rights law and people-centered justice; although they precede the advent of autonomous systems, they provide standards for evaluating discrimination, surveillance, and procedural fairness, and remain durable as new challenges to upholding justice and the rule of law emerge.

People-centered justice was not designed for legal systems addressing AI-related harms, but its outcome-driven orientation remains durable as new justice problems emerge.

The current stage presents an opportunity to align AI governance with access to justice from the outset. Beyond well-drafted rules, we need systems that people can use. And that means that any effective governance requires coordination between policymakers, legal professionals, and the public.

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

Key takeaways:

• • • Violence against judges is escalating 鈥� Targeted shootings, coordinated harassment campaigns, and threats that now routinely follow judges to their homes and families.

    • The rhetoric driving the escalation is coming from the highest levels of government 鈥� The absence of any public denunciation from the Department of Justice is highlighting the source of the problem.

    • Will the violence itself become part of judicial rulings? 鈥� The endgame of judicial intimidation isn’t that judges stop ruling, it’s that the threat of violence becomes a silent presence in the deliberation itself.

NEW YORK 鈥� Those attendees who came to the recent听 to talk about AI, agentic workflows, and the business of legal technology, also were treated to a session that will likely stay with attendees and had nothing to do with AI.

In that session, four federal judges took the stage; but they were not there to talk about pricing models or AI adoption. They were there to talk about staying alive.

Setting the stage

Jason Wareham, CEO of IPSA Intelligent Systems and a former U.S. Marine Corps judge advocate, introduced the session 鈥� a panel of four sitting United States District Court judges 鈥� by speaking of how the rule of law once seemed resolute, yet how that faith in that has been shaken, year after year. He worked hard to frame his observations as nonpartisan, a matter of institutional fragility rather than political allegiance. It was a generous framing, but it was one that would not survive the weight of the ensuing discussion.

The Honorable Esther Salas of the District of New Jersey said that the reason she was there has a name. On July 19, 2020, a disgruntled, extremist attorney who had a case before her court arrived at her home during a birthday celebration. He shot and killed her twenty-year-old son, Daniel Anderl. He shot and critically wounded her husband. She has spent the years since on a mission to protect her judicial colleagues from the same fate.

The new normal

Next, the Honorable Kenly Kiya Kato of the Central District of California described what has changed. Judges鈥� rulings are still based on the Constitution, on precedent, and on the facts; but what’s different is the small voice in the back of a judge’s head. That voice, often coming after a judge issued a decision that they now have to fight against, asks: What will happen after this? It is now expected, Judge Kato explained, that a high-profile order will bring threats. When two colleagues in her district issued prominent decisions, her first thought was for their safety. That is not how it has been historically.

The Honorable Mia Roberts Perez of the Eastern District of Pennsylvania asked how we got here, pointing to language from the highest levels of government: judges called monsters, a U.S. Department of Justice declaring war on rogue judges, and recently politicians bringing justice鈥檚 families into the conversation.

Judge Salas pushed even further. She acknowledged the instinct to frame the problem as bipartisan, but said the current moment is not apples to apples. It is apples to watermelons. The spike in threats since 2015, she argued, traces directly to rhetoric from political leaders using language never before deployed against the bench.

The federal judiciary is looking to break annual records for threats [against judges], and there is an absence of any public denunciation from the Attorney General or the DOJ.

The evidence is not abstract, nor are the victims, and the panel walked through it. Judge John Roemer of Wisconsin, zip-tied to a chair and assassinated in his home. Associate Judge Andrew Wilkinson of Maryland shot dead in his driveway while his family was inside. Judge Steven Meyer of Indiana and his wife Kimberly, shot through their own front door after attackers first posed as a food delivery, then returned days later claiming to have found the couple’s dog. Judge Meyer has just undergone his fifth surgery since the attack.

All of these incidents happened at the judges’ homes.

Judge Salas then played a voicemail, one of thousands that federal judges receive. It was less than 30 seconds long, but it did not need to be longer. While names had been redacted, what remained was a torrent of threats and obscenities, graphic, sexual and violent, delivered with the confidence of someone who does not expect consequences. Some judges receive hundreds of these after a single ruling, often from people with no case before them at all.

The shadow over the courts

Throughout the session, there was a presence the panelists circled but rarely named directly. A shadow that shaped every observation about escalating threats, every reference to rhetoric from the top down, every mention of language never before used by political leaders, of action or inaction the likes of which would have been unthinkable just several years ago. The specifics were spoken. The name, largely, was not.

It didn’t have to be.

Judge Kato said that what was perhaps the most disheartening aspect of all this is that these threats are getting worse. The people who know better are not doing better. Indeed, she said her children think about these problems every day. What will happen to mom today? Will someone come to the house? These are questions children should not have to carry. They did not sign up for this, and neither did the judges.

In 2026, Judge Salas noted, the federal judiciary is looking to break annual records for threats. She also noted the absence of any public denunciation from the Attorney General or the DOJ. The silence, she said, says a lot.

Not surprisingly, the implications extend beyond the judges themselves. As Judge Salas noted, if judges have to weigh their safety alongside the law, ordinary people don’t stand a chance. If one party is stronger, better funded, or more willing to threaten, then the scales tip.

That is the endgame of judicial intimidation. It鈥檚 not that judges stop ruling, but that the violent and the powerful 鈥� indeed, the people least fit to hold the scales 鈥� can tilt them at will.

That concern echoed an earlier warning from Judge Karoline Mehalchick of the Middle District of Pennsylvania. Judge Mehalchick said that judicial intimidation feeds on misunderstanding. When the public no longer grasps why judges must be insulated from pressure or conversely, mistakes independence for partisanship, the threat environment becomes easier to justify, easier to ignore, and harder to reverse.

What is perhaps the most disheartening aspect of all this is that these threats are getting worse, and the people who know better are not doing better.

In his 2024 year-end report, U.S. Supreme Court Chief Justice John Roberts identified four threats to judicial independence: violence, intimidation, disinformation, and threats to defy lawfully entered judgements. The panel discussed this report as prophecy fulfilled. Public confidence in the judiciary has plummeted since 2021, and the reasons are complex. The judges insisted they are still doing their jobs the right way, but the violence is spreading anyway.

What survives

Judge Salas asked the audience to watch their thoughts. Are they negative and destructive, or positive and uplifting? Can we start loving more? She ended by sending love and light to everyone in the room.

The judges were visibly emotional on the stage.

The words were beautiful. They were also, in the context of everything that had just been described 鈥� the killings, the voicemails, the zip ties, the pizza deliveries masking a threat under a murdered son’s name 鈥� resting in a shadow that no amount of love and light could fully dispel on their own.

The room responded with a standing ovation.

Thousands of people came to Legalweek 2026 to talk about the future of legal technology. For one morning, four judges reminded them that none of it matters if the people charged with administering justice cannot do so safely.

So, while the billable hour may survive and the associate will adapt, the harder question, the one that should keep the legal industry awake at night, is whether the bench will hold.

You can find more of听our coverage of Legalweek events听here

Key insights:

• • • Courts and the legal profession need to show leadership 鈥� Given their specialized knowledge of the needs of litigants and of courts, courts need to take the lead in determining definitions of the unauthorized practice of law.

    • 3 paths forward to workable regulatory solutions 鈥� Recent discussions and research around this subject offered three paths toward modernizing UPL definitions.

    • Uncertainty harms users and innovation 鈥� Fear of UPL can drive self-censorship and market exits, even as litigants continue to use publicly available GenAI tools.

Today, many Americans experience legal issues but lack proper access to legal representation. At the same time, AI tools capable of providing legal information are rapidly evolving and already in widespread use. Between these two facts lies a critical definitional problem that courts and state bars must urgently address: How to define the unauthorized practice of law (UPL) in way that doesn鈥檛 further curtail access to justice.

This discussion is not theoretical. It directly determines whether AI-based legal services can operate, how they should be regulated, and ultimately whether AI can help unrepresented or self-represented litigants gain meaningful access to justice. This issue was explored in more depth during a recent webinar from the听, a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI).

The need for clear definitions

During the webinar, Alaska Supreme Court Administrative Director Stacey Marz noted that “there is no uniform definition of the practice of law” and that UPL regulations represent “a real varied continuum of scope and clarity.” This variation makes compliance challenging for technology providers, especially as they navigate 50 different state standards.

UPL generally occurs when someone “not licensed as an attorney attempts to represent or perform legal work on behalf of another person,” explained Cathy Cunningham, Senior Specialist Legal Editor at 成人VR视频 Practical Law.

Marz added that such legal advice typically involves “applying the law, rules, principles, and processes to specific facts and circumstances of that individual client 鈥� and then recommending a course of action.”

The challenge, however, is that AI can appear to do exactly this, yet the regulatory framework remains unclear about whether and how this should be permitted and how consumers can be protected.

3 paths forward

During the recent webinar, panelists discussed several different approaches to UPL regulations, noting that a and outlined three approaches that state courts could take, including:

Path 1: Explicitly enabling tools with regulatory framework 鈥� UPL statutes can be revisited to explicitly allow purpose-built AI legal tools to operate without threat of UPL enforcement, provided they meet certain requirements. Prof. Dyane O’Leary, Director of Legal Innovation & Technology at Suffolk University, emphasized that consumer-facing AI legal tools are already being used for tailored legal advice, arguing that some oversight is better than “just letting these tools continue to operate and hoping consumers aren’t harmed by them.”

Path 2: Creating regulatory sandboxes 鈥� Courts could establish temporary experimental zones in which AI legal service providers can operate under controlled conditions while regulators gather data about efficacy and safety through feedback and research, with an eye toward informing future regulation reform.

Path 3: Narrowing UPL to human conduct 鈥� Clarifying that existing UPL rules apply only to humans who may hold themselves out as attorneys in tribunals or courtrooms or creating legal documents under the guise of being a human attorney, effectively would leave AI-powered legal tools clearly outside UPL restrictions and open up a “new pocket of the free market” for consumers.

Utah Courts Self-Help Center Director Nathanael Player referenced Utah Supreme Court Standing Order Number 15, which established their regulatory sandbox using a fundamentally different standard: Not whether services match what lawyers provide, but rather “is this better than the absolute nothing that people currently have available to them?”

Prof. O’Leary reframed the comparison itself, suggesting that instead of comparing consumers who use AI tools to consumers with an attorney, the framework should be “consumers that use legal AI tools, and maybe consumers that otherwise have no support whatsoever.鈥�

The personhood puzzle

“AI, at this time, does not have legal personhood status,鈥� said Practical Law鈥檚 Cunningham. 鈥淪o, AI can’t commit unauthorized practice of law because AI is not a person.”

However, Player pushed back on this reasoning, clarifying that 鈥淎I does have a corporate personhood. There is a corporation that made the AI, [and] the corporation providing that does have corporate personhood.” He added, however, that “it’s not clear, I don’t think we know whether or not there is鈥� some sort of consequence for the provision of ChatGPT providing legal services.”

You can view here

This ambiguity creates what might be called the personhood gap, a zone of legal uncertainty with serious consequences for both innovation and access to justice.

Colin Rule, CEO at online dispute resolution platform ODR.com, explained that “one of the major impacts of UPL is, actually self-censorship.” After receiving a UPL letter from a state bar years ago, he immediately exited that market. This pattern repeats across the legal tech landscape, leaving companies hesitant to innovate.

Rule’s bottom line resonates with anyone trying to build solutions in this space. “As a solution provider, what I want is guidance,鈥� Rules explained. 鈥淐larity is what I need most鈥� that’s my number one priority.”

Moving forward: Clarity over perfection

The legal profession needs to lead on this issue, and that means state bars and state supreme courts must take action now. The tools are already in use, and the question is not whether AI will play a role in legal services, but rather whether that role will be defined by thoughtful regulation or by default.

The solution is for the judiciary to provide clear guidance on what services can be offered, by whom, and under what conditions. To do that, courts much first acknowledge that for most people, the choice is not between an AI tool and a lawyer but between an AI tool and nothing. Given that, states must walk a path that will both encourage innovation and protect consumers.

To this end, legal professionals and courts should experiment with these tools, understand their trajectory as well as their current limitations, and work collaboratively with developers to create frameworks that prioritize consumer protection without stifling innovation that could genuinely expand access to justice.

You can find out more about how courts and legal professionals are dealing with the unauthorized practice of law here

Key insights:

• • • AI usage in courts needs verifiable reliability鈥� Unlike other fields, errors and hallucinations caused by AI in a court setting can create due-process issues.

    • Skepticism is professional responsibility鈥� Judges’ interrogation of AI sources and accountability concerns are vital guardrails to minimizing these problems.

    • Governance over perfection鈥� Courts and legal professionals should focus on systematic management of AI hallucinations through clear protocols, human oversight, and mandatory verification to ensure veracity.

AI hallucinations have become one of the most urgent and most misunderstood issues in professional work today; and as generative AI (GenAI) moves from and interesting experiment to common usage in many workplace infrastructures, these issues can cause significant problems, especially for courts and the professionals and individuals that use them.

Jump to 鈫�

Responsible AI use for courts: Minimizing and managing hallucinations and ensuring veracity

Today, AI can be used in everything from assisted research to guided drafting of documents, court briefs, and even court orders. With the development of tools supported by GenAI and agentic AI, the very infrastructure of professional work has shifted to include these offerings.

Yet, in most business settings, a wrong answer is an inconvenience. It requires minor corrections and has minimal impact. In the justice system, a wrong answer can be a due-process problem that strongly underscores the need for courts and legal professionals to ensure that their AI use is verifiably reliable when it counts.

At the same time, the direction of travel is clear: AI adoption isn’t a fad we can simply wait out, and it isn’t inherently at odds with high-stakes decision-making. Used well, these tools can reduce administrative burden, speed up access to relevant information, and help court professionals navigate large volumes of material more efficiently. The real question is not whether courts will encounter AI in their workflows, but how they will define responsible use, especially in moments in which accuracy isn’t a feature, it’s the foundation.

鈥淲hether you are a judge [or] an attorney, credibility is everything, particularly when you come before the court.鈥�

鈥� Justice Tanya R. Kennedy Associate Justice of the Appellate Division, First Judicial Department of New York

To examine these issues more deeply, the 成人VR视频 Institute has published a new report,听, which frames hallucinations not as a sensationalistic gotcha, but as a practical risk that must be managed with policy, process, and professional judgment. The report also features valuable insight on this subject from judges and court stakeholders who today are evaluating AI in the real operating environment of legal proceedings, courtroom expectations, and the daily administration of justice.

This perspective is essential. Technical teams can explain how models generate language and why they sometimes produce confident-sounding errors. However, judges and court staff can explain something equally important 鈥� what accuracy actually means in practice. In courts, accuracy isn’t just about getting the gist right; rather, it’s about precise citations, faithful characterization of the record, correct procedural posture, and language that withstands scrutiny. As the report points out, relied-upon hallucinated information isn鈥檛 merely bad output, it can lead to a potential distortion of justice.

Managing AI as professional responsibility

Crucially, the report reflects that judicial skepticism about AI is not simple technophobia 鈥� it’s professional responsibility. Judges are trained to interrogate sources, weigh credibility, and understand the downstream consequences of errors. Judges may ask, What is the provenance of this information? Can I reproduce it independently? And who is accountable if it’s wrong? These questions aren’t barriers to innovation; indeed, they are the guardrails that this innovation requires.

What emerges is a pragmatic middle ground that embraces the upside of AI use in courts while treating hallucinations as a predictable occurrence that can be managed systematically. Rather than concluding AI hallucinates, therefore AI can’t be used, the more workable conclusion is AI can hallucinate, therefore AI outputs must be designed, handled, and verified accordingly, likely with other advanced tech tools. As the report points out, courts don’t need a perfect AI; rather, they need repeatable protocols that keep human decision-makers in control and keep the record clean.

As the report ultimately demonstrates, managing hallucinations in courts isn’t about chasing perfection, it’s about protecting veracity. It’s about using the right advanced tech tools to build workflows in which the technology consistently supports the truth-finding process instead of quietly eroding it. And it’s about recognizing that in the legal system, responsibility doesn’t disappear when a new tool arrives 鈥� it becomes even more important to ensure the new tool doesn鈥檛 erode that either.

You can download

a full copy of the 成人VR视频 Institute’s 听here

Key takeaways:

• • • From scarcity to abundance 鈥� Technology has shifted the challenge in access to justice from scarcity of legal help to issues of accuracy, governance, and effective support.听AI and digital tools now provide abundant legal information to self-represented litigants, but they raise new questions about reliability, oversight, and alignment with human needs.

    • The necessity of human-in-the-loop 鈥� Human involvement remains essential for meaningful resolution.听While AI can explain procedures and guide users, real support often requires relational and institutional human guidance, especially for vulnerable populations facing anxiety, low literacy, or systemic bias.

    • One part of a bigger question 鈥� Systemic reform and broader approaches are needed beyond technological fixes because technology alone cannot solve deep-rooted inequities or the complexity of the legal system. Efforts should include prevention, alternative dispute resolution, and redesigning systems to prioritize just outcomes and accessibility.

Access to justice has long been framed as a problem of scarcity, with too few legal aid lawyers and insufficient funding forcing systems to be built in triage mode. This has been underscored with the unspoken assumption that most people navigating civil legal problems would do so without meaningful help, often because their issues were not compelling or lucrative enough to justify legal representation.

This framing no longer holds, however. Legal information, once tightly controlled by legal professionals, publishers, and institutions, is now abundantly available. Large language models, search-based AI systems, and consumer-facing legal tools can explain civil procedure, identify relevant statutes, translate dense legalese into plain language, and generate step-by-step guidance in seconds.

Increasingly, self-represented litigants are actively using these tools, whether courts or legal aid organizations endorse them or not. Katherine Alteneder, principal at Access to Justice Innovation and former Director of the Self-Represented Litigation Network, notes: 鈥淭his reality cannot be fully controlled, regulated out of existence, or ignored.鈥�

And as Demetrios Karis, HFID and UX instructor at Bentley University, argues: 鈥淲ithholding today鈥檚 AI tools from self-represented litigants is like withholding life-saving medicine because it has potential side effects. These systems can already help people avoid eviction, protect themselves from abuse, keep custody of their children, and understand their rights. Doing nothing is not a neutral choice.鈥�

Thus, the central question is no longer whether technology can help self-represented litigants, but rather how it should be deployed 鈥� and with what expectations, safeguards, and institutional responsibilities.

Accuracy, error & tradeoffs

The baseline capabilities of general-purpose AI systems have advanced dramatically in a matter of months. For common use cases that self-represented litigants most likely seek 鈥� such as understanding process, identifying next steps, preparing for hearings, and locating authoritative resources 鈥� today鈥檚 frontier models routinely outperform well-funded legal chatbots developed at significant cost just a year or two ago.

The central question is no longer whether technology can help self-represented litigants, but rather how it should be deployed 鈥� and with what expectations, safeguards, and institutional responsibilities.

These performance gains raise important questions about the continued call for extensive customization to deliver basic legal information. However, performance improvements do not eliminate the need for careful design. Tom Martin, CEO and founder of LawDroid (and columnist for this blog), emphasizes that 鈥渕inor tweaking鈥� is subjective, and that grounding AI tools in high-quality sources, appropriate tone, and clear audience alignment remains essential, particularly when an organization takes responsibility and assumes liability for the tool鈥檚 voice and output.

Not surprisingly, few topics in the legal tech community generate more debate than AI accuracy, but it cannot be evaluated in isolation. Human lawyers make mistakes, static self-help materials become outdated, and informal advice from friends, family, or online forums is often wrong. Models should be evaluated against realistic alternatives, especially when the alternative is no help at all.

Off-the-shelf tools now perform surprisingly well at generating plain-language explanations, often drawing on primary law, court websites, and legal aid resources. In limited testing, inaccuracies tend to reflect misunderstandings or overgeneralizations rather than pure fabrication. And while these are errors that are still serious, they may be easier to detect and correct with review.

Still, caution is key, often because AI tells people what they want to hear in order to keep them on the platform. Claudia Johnson of Western Washington University鈥檚 Law, Diversity, and Justice Center asks what an acceptable error rate is when tools are deployed to vulnerable populations and reminds organizations of their duty of care. Mistakes, especially those known and uncorrected, can carry legal, ethical, and liability consequences that cannot be ignored.

Knowledge bases are infrastructure, but more is needed

Vetted, purpose-built, and mission-focused solution ecosystems are emerging to fill the gap between infrastructure and problem-solving. The Justice Tech Directory from the Legal Services National Technology Assistance Project (LSNTAP) provides legal aid organizations, courts, and self-help centers with visibility into curated tools that incorporate guardrails, human review, and consumer protection in ways that general-purpose AI platforms do not.

Of course, this infrastructure does not exist in a vacuum. Indeed, these systems address the real needs of real people. While calls for human-in-the-loop systems are often framed as safeguards against technical failure, some of the most important reasons for human involvement are often relational and institutional. Even accurate information frequently fails to resolve legal problems without human support, particularly for people experiencing anxiety, shame, low literacy, or systemic bias within courts.

Not surprisingly, few topics in the legal tech community generate more debate than AI accuracy, but it cannot be evaluated in isolation.

A human in the loop can improve how self-represented litigants are treated by clerks, judges, and opposing parties. Institutional review models often provide this interaction at pre-filing document clinics, navigator-supported pipelines, and structured AI review workshops that integrate human judgment and augment human effort rather than replacing it.

Abundance and the limits of technology

Information does not automatically produce equity. Technology cannot make up for existing, persistent systemic issues, and several prominent voices caution against treating AI as a workaround for deeper system failures. Richard Schauffler of Principal Justice Solutions, notes that the underlying problem with the use of AI in the legal world is the fact that our legal process is overly complicated, mystified in jargon, inefficient, expensive, and deeply unsatisfying in terms of justice and fairness 鈥� and using AI to automate that process does not alter this fact.

Without changes at the courthouse level, upstream technological improvements may not translate into just outcomes. Bias, discrimination, and resource constraints cannot be solved by technology alone. Even perfect information from a lawyer does not equal power when structural inequities persist.

Further, abundance fundamentally changes the problem. As Alteneder notes, rather than access, the primary problem now is 鈥済overnance, trust, filtering, and alignment with human values.鈥� Similar patterns are seen in healthcare, journalism, and education. Without scaffolding, technology often widens gaps, benefiting those with greater capacity to interpret, prioritize, and act. For self-represented litigants, the most valuable support is often not answers, but navigation: What matters most now, which paths are realistic, how to understand when to escalate and when legal action may not serve broader life needs.

Focusing solely on court-based self-help misses an opportunity to intervene earlier, especially on behalf of self-represented litigants. AI-enabled tools have the potential to identify upstream legal risk and connect people to mediation, benefits, or social services before disputes harden.

You can find more insights about how courts are managing the impact of advanced technology from our Scaling Justice series here

Key insights:

• • • The legal system should avoid both overhyping and over-fearing AI 鈥� Instead, adopting a balanced approach that emphasizes careful, deliberate engagement and responsible experimentation.

    • Mandatory AI disclosure or certification orders do not necessarily improve the reliability of legal filings 鈥� In addition, they run the risk of creating confusion, false assurance, and additional hurdles, especially for smaller law firms and self-represented litigants.

    • Rather than imposing a restrictive order, the author issued guidance 鈥� This guidance is designed to promote responsible AI use, focusing on verification and accountability while allowing space for lawyers to engage with AI as a tool for augmentation rather than automation.

The legal system is being pulled in two directions when it comes to AI: On one side is overconfidence, the idea that AI will quickly solve legal work by automating it; and on the other side, fear 鈥� the feeling that AI is so risky that the safest response is to restrict it, discourage its use, or fence it off with new rules.

Both reactions are understandable; but neither is getting us where we need to go.

In a recent interview, Erik Brynjolfsson, the Director of the Stanford Digital Economy Lab and lead voice for the Stanford Institute for Human-Centered AI, makes that explain why both hype and too much skepticism miss the mark.

First, those caught up in the hype are moving too quickly toward automation. Tools work best when they support people, not when they try to stand in for them. Second, skeptics are overreacting to early stumbles. Early failures do not mean AI is a dead end. More often, they mean institutions are still learning how to use it well.

There is a middle ground. It鈥檚 not about rushing ahead, and it鈥檚 not about slamming the brakes. It鈥檚 about careful but deliberate use while testing tools, learning their limits, and moving forward with intention.

That perspective informs my approach.

Standing orders on AI

After well-publicized AI mistakes, it makes sense to look for something concrete that signals seriousness, and disclosure and certification orders do that. They tell the public and the bar that courts are paying attention. However, I don鈥檛 think disclosure does the work people hope it does, and I worry it pulls attention away from things that matter much more. I鈥檒l explain.

Disclosure does not make filings more reliable 鈥� Knowing whether a lawyer used AI to help draft a filing does not tell me whether that filing is accurate, complete, or well supported. Long before modern AI entered the picture, courts had to guard against overstated arguments, bad citations, and unsupported claims. Knowing which tools were used to prepare a filing did not make those filings or the tools more reliable then, and it does not make them more reliable now.

Certifications and disclosures may offer false assurance 鈥� The spotlight is on hallucinations (AI-generated fake cases or citations), but courts already have ways to identify and address those problems. The more concerning risks are quieter: bias, AI over-reliance, or subtle framing that influences how an argument is presented. I鈥檓 also extremely concerned about deepfakes, which are much more difficult to detect. Disclosure about AI use in briefs does not address any of those risks, and it may distract us from the far bigger risks. It also creates a false sense that a filing is more careful or reliable than it actually may be.

Additional orders can add confusion 鈥� AI standing orders are growing in number, and they take very different approaches. Some require disclosure, some certifications, some limits, some are outright bans. Definitions vary or are missing altogether. Lawyers can comply, but it takes time and careful reading, and as noted already, it doesn鈥檛 necessarily improve the quality of what reaches the court.

Early in my time as a United States Magistrate Judge, I made it a point to seek feedback from the legal community about what made legal practice more difficult than it needed to be. One theme came up repeatedly 鈥� keeping track of multiple, overlapping judicial practice standards was tough. In response, I worked with my colleagues to consolidate standards into a single, uniform set. I see a similar risk emerging with AI standing orders. Well-intentioned but divergent approaches can splinter practices and create new hurdles, particularly for smaller law firms and self-represented litigants. I don鈥檛 want to issue a standing order that adds another layer of complexity without meaningfully improving the quality of what comes before me.

The rules already cover the landscape 鈥� I already have tools to deal with inaccurate or misleading filings. Lawyers are responsible for the work they submit, and Rule 11 doesn鈥檛 stop working because AI was involved. If something is wrong or misleading, I already have ways to address it.

Certification or disclosure could be misinterpreted as discouraging AI use, and I worry about who gets left out 鈥� When new tools are treated as suspect or off-limits, those with the most resources find ways to keep moving forward. However, smaller firms and individual litigants fall further behind. A system that chills responsible experimentation risks widening access gaps instead of narrowing them. In my view, everyone should be exploring ways to, as Brynjolfsson says, 鈥渁ugment鈥� themselves. So long as we remain accountable for the result, augmentation is how lawyers, judges, and other professionals will retain their value in a legal system that is becoming more AI-integrated every day.

Rather than issue a standing order that limits AI use or requires certification or disclosure, I offer : Check your work, protect confidential information, and take responsibility for what you submit. I published this guidance for those interested in my perspective, but it is deliberately not an order, so as to avoid the concerns described above.

We shouldn鈥檛 fear AI 鈥� we should shape it

Some warn that AI is coming for the legal profession; however, I鈥檓 more optimistic (and perhaps more idealistic).

In my view, the justice system depends on human judgment. Empathy, discretion, humility, moral reasoning, and uncertainty are not bugs in the system, rather they鈥檙e an essential part of the program. If we want to preserve human judgment in the age of AI, we must be involved in how AI is used. And we can鈥檛 do that from a distance. We have to engage with AI, understand its limits, and model responsible use.

Used carefully, AI can help judges:

• • • organize large records,
    • identify gaps or inconsistencies,
    • spot issues that need a closer look,
    • identify and locate key information,
    • translate legal jargon to help self-represented litigants better understand what is being asked of them, and
    • reduce administrative drag so more of a judge鈥檚 time is spent on decision-making.

This kind of use does not replace us; rather, it supports us. It augments us so we do our work as well as we can, help as many people as possible, and still keep human judgment at the center of everything.

Why this moment matters

The AI conversation in law will remain noisy for a while. Some legal professionals will promise too much. Others will warn against everything. The better path is in the middle 鈥� engage, test, verify, and adjust.

As the Newsweek article suggests, this is a watershed moment. Not because AI will decide the future of our institutions, but because we will. The choices we make now will shape what AI does in the justice system, and just as importantly, what it does not do.

We should not be afraid of AI. We should help shape how it is used so it strengthens, rather than replaces, the human judgment at the heart of the legal justice system.

You can find out more about how courts and the legal system are managing AI here