AI can help address the access to justice crisis by assisting unrepresented litigants who cannot afford legal representation. Millions worldwide struggle to navigate complex legal systems designed for legal professionals, resulting in fundamental inequalities in access to justice. The chapter introduces how legal AI assistants can help unrepresented litigants understand their rights, complete legal documents, and navigate court procedures.

The analysis covers a range of technologies, including generative AI, machine learning, computational logic, document automation systems, and more.

Beyond technological tools, the chapter addresses critical barriers, including unauthorized practice of law restrictions, and emphasizes the need for institutional reforms. The chapter argues that legal AI assistants should be treated as public goods, similar to courts and schools, and that a coordinated effort between developers, legal practice bodies, courts, and policymakers is necessary to effectively democratize access to justice and ensure equality under the law.

This chapter examines two dimensions of AI assurance for systems designed to assist unrepresented litigants: (1) reliability and (2) privacy and accountability. Reliability encompasses accuracy, calibration, robustness, and bias. Privacy and accountability involve balancing data needs with the protection of sensitive information and ensuring the responsible deployment of AI. These considerations take on heightened importance given unrepresented litigants’ unique vulnerabilities and legal contexts.

Reliability standards must be higher for unrepresented litigants, who cannot verify AI outputs, than for legal professionals, who can exercise oversight. Privacy and accountability challenges arise from AI systems’ need for vast training datasets containing sensitive legal information, creating tensions between the effectiveness of these systems and data protection rights under frameworks such as the General Data Protection Regulation (“GDPR”) and the EU AI Act.

Effective AI assurance requires interdisciplinary collaboration between technologists and legal stakeholders to develop appropriate methodologies for defining, measuring, and verifying both reliability and privacy protections in legal AI systems.

This chapter investigates the global access to justice crisis, where approximately 1.4 billion people worldwide have unmet civil or administrative justice needs despite living in functioning legal systems. Through an analysis of data from multiple countries including across North America, South America, Europe, and Asia, the chapter demonstrates that individuals with legal problems often struggle to access adequate assistance. Unrepresented litigants face substantial disadvantages without legal support, including, losing custody of children, facing eviction, and experiencing asset seizure.

Four factors are exacerbating this crisis: growing societal complexity, which creates increasingly intricate legal procedures; overprofessionalization of the legal community; systemic bias favoring institutional plaintiffs; and the fundamental language gap separating laypeople from lawyers. Addressing this crisis is imperative for two primary reasons: fairness and social costs. The chapter proposes that legal AI systems represent public goods, similar to courts and schools, requiring coordinated investment to democratize access to justice.

Online Dispute Resolution (“ODR”) emerged from the convergence of Alternative Dispute Resolution practices and digital platforms, addressing the need for more accessible dispute resolution mechanisms. ODR platforms reduce logistical burdens by eliminating travel requirements and enabling asynchronous communication, while helping to level the playing field between represented and unrepresented parties.

This chapter examines how AI enhances ODR through automated settlement calculations using game theory, negotiation analysis through discourse monitoring, and sentiment analysis to detect contentious exchanges. AI systems can also assist facilitators by analyzing negotiation patterns and suggesting when interventions may be needed.

Despite significant potential benefits, ODR faces challenges, including the digital divide, algorithmic bias, and jurisdictional enforceability. Well-implemented ODR platforms, supported by transparent standards and ethical AI principles, may significantly contribute to making dispute resolution more equitable and responsive to modern societal needs, while requiring careful attention to user-centered design and robust data protection measures.

No single AI system or other technology can address all the challenges unrepresented litigants face when navigating the legal system. This chapter highlights the strengths and weaknesses of integrating various technological approaches, including generative AI, computational logic, machine learning, document automation, and dialogue systems.

Through the hypothetical case of Jaime, a tenant facing unsafe and unlivable housing conditions, the chapter illustrates how integrated AI systems can guide unrepresented litigants through every stage of legal proceedings, from initial problem recognition to case resolution. The analysis shows how chatbots may help engage legal consciousness, generative AI may assist in bridging the language gap, computational logic can ensure procedural accuracy, and machine learning can provide case analysis and outcome prediction.

However, the chapter also addresses significant limitations of integrated approaches, including compounding errors across systems, cumulative biases, and substantial resource requirements for development and maintenance. While integrated AI systems offer promising potential for expanding access to justice, their practical implementation faces considerable financial and organizational constraints that may limit widespread adoption.

This chapter introduces a task-based framework for understanding the stages of civil litigation, from pre-litigation claim recognition through resolution, focusing on the challenges that unrepresented litigants face with each task. The framework provides a foundation for evaluating the potential of AL and other technologies to assist with each task. The chapter identifies recurring themes across tasks, including the need for dynamic adaptation to case developments, the overlap in tasks performed by plaintiffs and defendants, and the limitations of technology for tasks requiring direct human interaction. These insights set the stage for subsequent chapters that explore specific technological innovations and their applications in civil litigation.

Through case studies and interviews, this chapter explores both the transformative potential and the implementation challenges of AI and other technologies in today’s access to justice systems. It features profiles of legal technology initiatives transforming how unrepresented litigants access justice, from British Columbia’s Civil Resolution Tribunal, which handles over 52,000 cases through fully remote proceedings, to AI-powered chatbots like JusticeBot and Rentervention, which serve thousands of users.

The chapter examines nonprofit and public sector initiatives, including Pro Bono Net’s LawHelp Interactive, Upsolve’s bankruptcy assistance platform, Kansas’s Protection Order Portal, Nevada’s Self-Help Chatbot, and Suffolk University’s Document Assembly Line Project, as well as commercial platforms such as Hello Divorce, Courtroom5, and Quest for Justice. These initiatives demonstrate technology’s potential to scale legal assistance.

However, significant challenges remain, including sustainability as crisis funding expires, language access, and unauthorized practice of law restrictions. Their long-term success depends on sustained funding commitments and institutional willingness to embrace technological innovation that makes justice more accessible to all.

Computational logic is a rule-based approach that encodes laws and procedures into structured logical rules, and this chapter examines its use in expanding access to justice. By encoding laws into structured if–then statements, these systems translate complex legal language into navigable decision trees for unrepresented litigants. This technology is particularly effective in procedurally clear domains, such as tax and housing law and protection orders, offering transparent reasoning that users can understand and verify.

This chapter examines how computational logic reduces costs, enhances efficiency, and facilitates explainable decisions, but it also highlights several limitations: its rigid structures struggle with ambiguous legal concepts, cannot provide counterfactual reasoning, and may perpetuate unjust laws through consistent application.

Future developments are exploring hybrid approaches that combine the transparency of computational logic with the adaptability of machine learning, particularly through case-based reasoning. While these systems cannot replace human legal judgment, they offer powerful tools for democratizing access to legal guidance in procedurally defined domains.

Document automation technology converts static court forms into dynamic, guided interviews. This chapter examines how automated systems address barriers to justice, including legal complexity, identifying the correct form for a legal issue, and procedural errors that prevent individuals from effectively asserting their rights.

Document automation is both a practical tool for expanding access to justice and a foundation for more sophisticated AI-enhanced legal assistance tools. The technology operates through template-based platforms that break down intimidating legal documents into plain-language questions, automatically populate multiple related forms from single data entries, and validate information in real time to prevent filing errors. Document automation platforms, built using Docassemble, A2J Author, HotDocs, and related tools, have enabled users worldwide to complete legal forms with greater accuracy than traditional manual methods typically allow.

While current systems excel at structural automation and form completion, limitations remain in handling complex narrative drafting and substantive legal reasoning. The chapter examines the emerging applications of artificial intelligence and large language models in enhancing template creation, automating data extraction, and improving narrative generation.

My argument is that it is impossible for a judge to both provide unrepresented and poorly qualified litigants with meaningful legal assistance while at the same time avoiding impartiality. The English system of civil justice offers poor access to litigants of limited means. This has been the case for a long time, but the problem has become even more serious since the abolition of legal aid in civil cases and there it is now urgent to address it. The strategy that seems to appeal to the legislator favours procedures that can be conducted without the involvement of professional advocates, of inquisitorial proceedings. This strategy is misconceived. While there is much to be said for simplifying the litigation system through use of technology, online resources and other labour-saving measures, there is no substitute for adversarial process.