Platforms and legal tech tools have enabled new ways to draft, manage, conclude, and monitor contracts. This chapter will focus on contract performance management tools and automated systems for monitoring contract execution. Thanks to the granularity offered by technology, broader and more detailed monitoring is possible, even at the contract execution phase. These may include using key performance indicators (KPIs), feedback systems to automatically identify performance gaps and compliance verification mechanisms. Introducing tools for monitoring contractual performance may prevent disputes by providing a more precise qualification of non-compliance. Contract performance management tools can also be used in dispute resolution mechanisms, which can intervene in situations where the KPIs have already been identified. After an introduction to some of these tools and solutions and a description of the relevant software, this analysis will examine the effects on contract practices and contract law, focusing mainly on (a) performance determination and non-performance contestation, and (b) monitoring infringements of mandatory rules or policies with contract compliance.

This chapter offers an overview of how artificial intelligence (AI) systems are being used (and could potentially be used) within the context of public courts to help predict legal outcomes, generate content such as motions and other court documents, make recommendations to legal stakeholders regarding positions to adopt (including whether to go forward with a suit) or become a substitute to human judges in the decision-making process. Looking at all these potential scenarios, the authors present the pros and cons of relinquishing a portion of legal stakeholders’ autonomy to AI systems, focusing on underlying risks linked to bias, datasets and a pervasive misunderstanding of what AI system are, and what they can do. The chapter suggests that, while the use of AI can, in many instances, positively affect impediments to access to public courts such as delays, and cost, legal stakeholders need to better understand how this can impact fundamental legal tenets.

This paper delves into the viability of artificial intelligence (AI) as a legal decision-maker through a controlled experiment involving two advanced language models: GPT-4o and Llama 3.1. Using a real-world Colombian arbitration case centred on contract disputes exacerbated by the COVID-19 pandemic, the authors test each model’s ability to generate legally sound arbitration awards. The experiment unfolds in two scenarios: the first provides only the factual background of the case and the second includes the legal arguments of both parties. Each model’s output is evaluated against Colombian legal standards to determine whether the resulting decisions could withstand annulment proceedings. The findings show that GPT-4o closely mirrors the reasoning and outcome of the actual arbitral award, applying doctrines like imprevisión and good faith with legal coherence. Llama 3.1, while capable of reproducing basic legal reasoning, displays limitations, particularly when limited to factual inputs. The results demonstrate that state-of-the-art AI can, under proper conditions, replicate complex legal reasoning and generate decisions unlikely to be overturned. The paper concludes that AI has the potential to serve as a decision-maker in certain adjudicatory contexts, warranting further exploration into its regulated use in arbitration and beyond.

Mediation is an alternative dispute resolution (ADR) mechanism where a neutral third party intervenes in a dispute to help the parties achieve their goals, such as finding an agreement. In this chapter, we examine how artificial intelligence (AI) can be used to further enhance and expand the process of mediation. We explore a variety of different integration points between AI and mediation as illustrated by academic research projects, focused on supporting the disputants and mediator. Then, we discuss some overall insights that can be gained from these projects, including opportunities and challenges that arise when integrating AI in mediation. Overall, we see AI as having significant potential in both increasing the efficiency of mediation and introducing new elements to mediation. Hopefully, these integrations will increase the accessibility and further enhance the benefits of mediation, thus contributing to a more harmonious society.

The Brazilian judicial system, notable for its extensive scope and intricate structure, grapples with significant operational challenges, including a vast backlog of cases and prolonged resolution times. Addressing these issues calls for innovative approaches, with the integration of artificial intelligence (AI) emerging as a viable solution. This chapter investigates the integration of AI within the Brazilian judiciary, providing an analysis of its implementation, challenges, and outcomes. The chapter begins by outlining the complexities of the Brazilian judiciary and the initial steps towards AI integration, with initiatives that have paved the way for various AI applications, from predictive analytics to natural language processing, aimed at improving decision-making and case management. Key case studies are examined to highlight AI’s practical benefits and limitations in the judiciary. Notable projects demonstrate improvements in efficiency and accuracy. However, they also underscore the ongoing challenges related to interoperability, data privacy, and ethical considerations. In conclusion, the chapter reflects on the future of AI in the Brazilian judiciary, emphasising the need for strategic planning, robust governance, and continuous evaluation to ensure that AI integration not only enhances efficiency but also upholds justice and fairness.

President Biden’s Executive Order 14110, ‘Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence’, set a comprehensive policy for the US government to address concerns arising from AI technologies aiming to harness them for the public good. Despite its revocation by the current administration, we argue that the work completed under this Executive Order remains highly relevant for AI governance in civil litigation and beyond. Its deliverables – such as the National Institute of Standards and Technology’s Generative Artificial Intelligence Profile – continue to serve as critical resources for courts, law and policymakers, the private sector, and state and local governments. As AI increasingly permeates civil litigation, legal practitioners and courts are turning to these resources to establish evidentiary standards, assess algorithmic accountability, and navigate ethical concerns surrounding AI use. In the absence of federal governance, the revoked Executive Order’s focus on risk management, civil rights, and adaptive oversight may influence state law, judicial reasoning, and corporate self-regulation, indirectly becoming foundational tools for AI’s responsible integration into justice and society.

This chapter explores China’s innovative integration of artificial intelligence (AI) into its judicial system through the establishment of Internet Courts. As part of China’s broader digital transformation strategy, these courts utilise AI technologies, such as virtual courtrooms, intelligent case filing, automated document review, and blockchain-based evidence storage to improve efficiency, accessibility, and transparency in legal proceedings. Based on performance data and interviews with judges and legal practitioners, the paper evaluates how AI is reshaping judicial decision-making and court operations. While AI has enhanced speed, consistency, and administrative capacity, concerns persist over algorithmic bias, data quality, and lack of transparency in decision-making processes. Case studies from Internet Courts in Hangzhou, Beijing, and Shanghai demonstrate both the practical benefits and the systemic limitations of AI in adjudication. The paper argues that although AI holds significant promise, it must be implemented with robust oversight, legal safeguards, and meaningful human involvement to prevent over-reliance. Preserving judicial discretion, ethical standards, and human empathy is crucial to ensuring that technological progress does not undermine fairness, public trust, or legitimacy in the justice system. The study contributes to broader debates on the responsible governance of AI in law and the future of judicial automation.

This chapter explores the intersection of artificial intelligence (AI) and dispute system design (DSD), emphasising AI’s dual role in both generating and resolving civil disputes. It begins with the notion of civil justice, then introduces a general analytic framework for DSD, detailing how conflicts – ranging from neighbourhood disputes to campus tensions – can be managed through facilitative and adjudicative processes, both online and offline. The chapter examines AI’s potential as a third and fourth party in dispute resolution, discussing its applications in predicting conflicts, enhancing negotiation, and designing conflict management systems. Through case studies in social networking, community disputes, and student conflicts, the chapter highlights AI’s ability to analyse data, provide mediation services, and improve accessibility, while also addressing concerns of fairness, accountability, and privacy in technologically driven dispute resolution.

This chapter explores the evolving role of artificial intelligence (AI) in the justice system. AI tools are increasingly used across legal domains – from document generation and case management to assisting in negotiation and even supporting judicial decisions. While earlier AI applications focused on automating routine tasks, today’s large language models (LLMs) demonstrate capabilities once considered distinctly human, such as legal reasoning, persuasive communication, and emotional sensitivity. We review recent empirical studies examining public perceptions of AI in legal settings. These findings reveal a nuanced picture: human decision-makers are often preferred for their empathy and discretion, while AI is valued for consistency, efficiency, and neutrality – especially in low-stake disputes. Interestingly, there is growing acceptance of AI even in roles that require managing interpersonal dynamics, such as facilitating agreement or moderating emotionally charged interactions. As AI capabilities continue to expand, the boundary between automated and human-driven legal processes is becoming less distinct. Perceptions of fairness, trust, and legitimacy will shape how legal actors and the public respond to AI’s presence in justice systems. We conclude by raising critical questions about what is gained and lost as technology becomes more deeply embedded in dispute resolution and legal decision-making.

The chapter explores the impact of the use of AI in civil dispute resolution on the public/private divide. It argues that the introduction of digital technologies, and particularly AI, complicates traditional boundaries between public and private institutions, reshaping both values and institutional dynamics. The chapter unveils a dual trend: on the one hand, private-sector involvement in public courts can result in a creeping privatisation, impacting the allocation of technical expertise, the power to shape procedural law, and the ability to affect fundamental rights. On the other hand, technology can also drive a publicisation of private dispute mechanisms, with private actors adopting public-oriented goals. The chapter identifies three AI-driven disruptions of the public/private balance: (a) the extraction of value from publicly generated legal data by private AI developers; (b) the potential of AI to constrain judicial discretion and independence by reinforcing precedent-based reasoning; and (c) the lack of context sensitivity in AI systems, which necessitates a broader understanding of human–machine interactions. Therefore, the chapter argues for a careful delimitation of AI’s role in civil dispute resolution, so as to protect fundamental rights and values, as well as ensuring that human judgement remains central.

Technology has become the ‘fourth party’ in dispute resolution through the growing field of online dispute resolution (ODR), which includes using a broad spectrum of technologies in negotiation, mediation, arbitration, and everything in between. Furthermore, AI has become a particularly powerful fourth party, and may even become the third party in some situations where AI makes the decision. Accordingly, it is imperative that professionals and policymakers tread cautiously and remain responsible in their use of AI in dispute prevention and resolution. This chapter will discuss foundational considerations around the benefits and risks of AI in dispute resolution, and the regulations as well as ethical guidelines that should remain a top priority when using AI in civil dispute resolution.

This concluding chapter synthesises insights from across The Cambridge Handbook of AI in Civil Dispute Resolution, offering a forward-looking reflection on the ethical, institutional, and technological dimensions of AI integration in civil justice systems. It traces the evolution of AI in dispute resolution – from rule-based automation in e-commerce to the emergence of agentic AI – and evaluates how foundational principles such as transparency, accountability and human-centred design must guide future developments. Drawing on the book’s thematic parts, the chapter emphasises the importance of hybrid human–AI collaboration, stakeholder-driven system design and robust governance frameworks. It warns against over-reliance on opaque technologies and highlights the need for legal professionals to maintain core skills in empathy, discretion, and communication. Ultimately, the chapter calls for a principled approach to AI adoption that enhances, rather than undermines, fairness and access to justice in both public and private dispute resolution contexts.

This chapter examines the evolution and deployment of AI tools in the delivery of dispute resolution in sub-Sahara Africa (SSA) with particular focus on arbitration. The chapter draws on publicly available original data to argue that there indeed is greater opportunity to deploy AI in arbitration as a tool for efficiency, which may lead to cost and time savings. It also explores the emerging regulation of these tools globally, regionally, and in some SSA countries and concludes that regulation of the use of AI must maintain the right balance of achieving efficiency in the process of arbitration and mitigation of its negative effects.

The chapter examines the adjudication of AI-related disputes as well as the application of AI-driven technologies in international commercial courts (ICommCs), a relatively new adjudication forum. It argues that ICommCs are well-suited for resolving digital technology disputes due to their publicness, transparency, and capacity to develop jurisprudence for the digital economy – advantages that set them apart from ADR and ODR mechanisms. Their international nature also aligns with the transnational character of digital disputes. Additionally, ICommCs are ideal for integrating AI-driven innovations in dispute resolution, as they are more agile and adaptable than other forums, particularly ordinary domestic courts. Their specialised judges, manageable caseloads, and ability to swiftly address emerging technological challenges further enhance their suitability.

This chapter examines the early integration of generative AI (GenAI), particularly large language models (LLMs) like ChatGPT, into judicial workflows. Unlike traditional rule-based decision-support systems, GenAI adopts a bottom-up approach, generating insights from vast datasets to assist real-time decision-making. While offering speed and improved access to information, these tools also present challenges that require careful understanding by their users. Using the recent case of a Dutch judge who employed ChatGPT to estimate the lifespan of solar panels, the chapter illustrates how GenAI is already being used in courtrooms. The value of GenAI lies in supporting, not replacing, human judgement. Yet without a clear grasp of how these systems work, including their limitations and potential biases, judges risk relying on opaque or flawed outputs. The ‘black box’ nature of LLMs complicates their responsible use and raises concerns about the balance between efficiency and discretion. The chapter argues that effective integration of GenAI depends not primarily on regulation, but on judicial education and critical awareness of the technology’s capacities and constraints.