David Engstrom and Jess Lu (both Stanford Law) first show that an otherwise fast-growing and dynamic “legal tech” industry has not generated significant “direct-to-consumer” technologies designed to help self-represented litigants navigate a complex legal system. They then interrogate that puzzle: Why is it that better consumer legal tech hasn’t flourished? They ultimately settle on the idea that rule reforms alone may not stimulate high-scale, direct-to-consumer technology. Instead, other policy interventions may be necessary, including standardizing what is currently a checkerboard of court technology and data infrastructures. Perhaps more importantly, direct-to-consumer legal tech may have trouble overcoming some of the problems that are inherent to markets that are attempting to serve individuals with episodic attachment to the civil justice system and limited ability to pay. The result is an important meditation on whether reforms to UPL, Rule 5.4, or something else entirely are necessary to unlock the potential of potent new technologies in order to narrow the justice gap.

This chapter presents a comprehensive workflow for applying network machine learning to functional MRI connectomes. We demonstrate data preprocessing, edge weight transformations, and spectral embedding techniques to analyze multiple brain networks simultaneously. Using multiple adjacency spectral embedding (MASE) and unsupervised clustering, we identify functionally similar brain regions across subjects. Results are visualized through abstract representations and brain-space projections, and compared with established brain parcellations. Our findings reveal that MASE-derived communities often align with known functional and spatial organization of the brain, particularly in occipital and parietal areas, while also identifying regions where functional similarity doesn’t imply spatial proximity. We illustrate how network machine learning can uncover meaningful patterns in complex neuroimaging data, emphasizing the importance of combining algorithmic approaches with domain expertise to motivate the remainder of the book.

Rebecca Sandefur (Arizona State) and Mathew Burnett (American Bar Foundation) – one a MacArthur Genius Award-winning sociologist, the other a longtime leader on access-to-justice issues – explore ways to reform legal services regulation, from relaxing UPL rules (to welcome new providers into the system) to relaxing Rule 5.4’s bar on nonlawyer ownership of law firms (to make available new sources of capital investment). After reviewing existing empirical evidence, they argue in favor of the former, in order to spur new human-centered service models, as against longer-term and less proven reforms altering law firm ownership.

This chapter introduces the network machine learning landscape, bridging traditional machine learning with network-specific approaches. It defines networks, contrasts them with tabular data structures, and explains their ubiquity in various domains. The chapter outlines different types of network learning systems, including single vs. multiple network, attributed vs. non-attributed, and model-based vs. non-model-based approaches. It also discusses the scope of network analysis, from individual edges to entire networks. The chapter concludes by addressing key challenges in network machine learning, such as imperfect observations, partial network visibility, and sample limitations. Throughout, it emphasizes the importance of statistical learning in generalizing findings from network samples to broader populations, setting the stage for more advanced concepts in subsequent chapters.

Neil Steinkamp and Samantha DiDimenico, strategic consultants who have done extensive work on access-to-justice issues, offer a unique how-to guide for engaging courts and community stakeholders in order to generate quantitative and qualitative data that can contribute to reform efforts. Focusing on “civil Gideon,” a growing set of efforts to establish a “right to counsel” akin to what criminal defendants have long enjoyed under the Sixth Amendment, Steinkamp offers a step-by-step roadmap for developing an empirically rigorous and comprehensively informed dialogue toward regulatory reform.

Judge Carolyn Kuhl (L.A. Superior Court), until recently the chief judge of the nation’s largest trial court system, offers an important contribution to the debate about whether and how to relax “courthouse UPL” – the possibility that judges, court clerks, other court staff, and AI-enabled chatbots might plausibly narrow the justice gap by providing self-represented litigants with necessary assistance. At once a history lesson and an in-the-trenches look at a decade of L.A. court reforms, Judge Kuhl shows how the anxieties about judicial and court neutrality have given way to a rich array of reform options that are producing concrete lessons for other judicial reformers looking for alternatives to conventional forms of legal help.

This chapter presents a framework for learning useful representations, or embeddings, of networks. Building on the statistical models from Chapter 4, we explore techniques to transform complex network data into vector representations suitable for traditional machine learning algorithms. We begin with maximum likelihood estimation for simple network models, then motivate the need for network embeddings by contrasting network dependencies with typical machine learning independence assumptions. We progress through spectral embedding methods, introducing adjacency spectral embedding (ASE) for learning latent position representations from adjacency matrices, and Laplacian spectral embedding (LSE) as an alternative approach effective for networks with degree heterogeneities. The chapter then extends to multiple network representations, exploring parallel techniques like omnibus embedding (OMNI) and fused methods such as multiple adjacency spectral embedding (MASE). We conclude by addressing the estimation of appropriate latent dimensions for embeddings. Throughout, we emphasize practical applications with code examples and visualizations. This unified framework for network embedding enables the application of various machine learning algorithms to network analysis tasks, bridging complex network structures and traditional data analysis techniques.

This appendix provides a concise introduction to key machine learning techniques employed throughout the book. It focuses on two main areas: unsupervised learning and Bayesian classification. The appendix begins with an exploration of K-means clustering, a fundamental unsupervised learning algorithm, demonstrating its application to network community detection. It then discusses methods for evaluating unsupervised learning techniques, including confusion matrices and the adjusted Rand index. The silhouette score is introduced as a metric for assessing clustering quality across different numbers of clusters. The appendix concludes with an explanation of the Bayes plugin classifier, a simple yet effective tool for network classification tasks.

Rebecca Aviel (Denver University (Sturm) Law) draws on her deep expertise in family law to illuminate ways in which domestic relations cases are exceptional relative to other legal areas where access concerns are acute. Family law’s exceptionalism, she contends, justifies thoroughgoing changes to that system’s adversarial architecture, such as permitting a single lawyer to represent both sides in a divorce, that are well-tailored to family law even if nonstarters in other parts of the civil justice system. Aviel also suggests that some innovative family law programs might travel well, informing reforms in other civil justice contexts even where they cannot be directly replicated.