This chapter explores the potential for gamesmanship in technology-assisted discovery.1 Attorneys have long embraced gamesmanship strategies in analog discovery, producing reams of irrelevant documents, delaying depositions, or interpreting requests in a hyper-technical manner.2 The new question, however, is whether machine learning technologies can transform gaming strategies. By now it is well known that technologies have reinvented the practice of civil litigation and, specifically, the extensive search for relevant documents in complex cases. Many sophisticated litigants use machine learning algorithms – under the umbrella of “Technology Assisted Review” (TAR) – to simplify the identification and production of relevant documents in discovery.3 Litigants employ TAR in cases ranging from antitrust to environmental law, civil rights, and employment disputes. But as the field becomes increasingly influenced by engineers and technologists, a string of commentators has raised questions about TAR, including lawyers’ professional role, underlying incentive structures, and the dangers of new forms of gamesmanship and abuse.4

Every day, courts across the country deliver rulings in civil legal disputes that, while perhaps unremarkable to many observers, have profound importance for the litigants themselves: custody disputes, debt collection, and wage garnishment, evictions, and protection orders for victims of domestic violence, to name but a few. Advocates for civil justice reform have long identified the growing number of self-represented litigants in these proceeding as a cause for concern, highlighting the need for broad reform. The number of self-represented litigants hasn’t decreased, but reform has been slow to come. The COVID-19 pandemic has offered a chance to change that. This chapter reviews civil access-to-justice efforts both before, during, and hopefully after the pandemic. Using as raw material a unique survey of state chief justices and also Michigan’s experience both before and during the pandemic to make justice more accessible, this chapter examines barriers to widespread reform in state courts, including the challenge of local political and fiscal control in states with non-unified court systems as well as the broader challenge of promoting change in a profession that is notoriously slow to embrace it. In small ways and large, the pandemic will continue to drive important change in how courts deliver justice.

The United States has a serious and persistent civil justice gap. Computationally driven litigation outcome prediction tools might offer a solution by reducing uncertainty and lowering the cost of legal services. Yet the field remains in its infancy: this chapter identifies the data, methodological, and financial limits that have impeded development in general and the potential to expand access to justice in particular. The chapter also raises a note of caution about unintended consequences. As outcome prediction reaches maturity, such tools might reify existing case outcome patterns and lock out litigants whose claims are novel or boundary-pushing. This legal endogeneity may reduce access to justice for some categories of would-be litigants and diminish the flexibility and adaptability that characterize common law reasoning. Empirical questions remain about the way(s) that outcome prediction might affect access to justice. Yet if developments continue, policymakers and practitioners should be ready to exploit the tools’ substantial potential to fill the civil justice gap while also guarding against the harms they might cause.

How can improving the collection, sharing, and analysis of data make the civil justice system more accountable to other government institutions, participants in the justice system, and the public at large? We tackle this question from three angles. First we show how accountability can create opportunities for civil justice reform. Drawing on work in other social spheres on large datasets, we identify three lines of research that court data could inform: the extent that structural racism and other biases shape processes and outcomes; the impact of lack of representation on litigants’ experiences and outcomes; and the antecedents and consequences of court involvement for poor people. A second focus is the obstacles that prevent us from increasing our store of knowledge about civil justice problems. These obstacles include: the lack of good data, legal barriers to obtaining data, and real and perceived institutional risks to sharing data. Finally, we report on our efforts to design and build a civil justice data commons (CJDC) addressing these barriers in order to provide fast and frictionless access for policy research as well as operational insights for courts and civil justice institutions to improve equity and service delivery.

MDLs rely, for legitimacy, on the notion that the individual litigant calls the shots. That fact justifies a system that affords MDL litigants few, if any, safeguards, even while furnishing class members in class actions elaborate procedural protections. In this Chapter, we zero in on litigant autonomy in MDLs. We explain why autonomy matters, dissect its components, and evaluate how much autonomy MDL litigants seem to have in practice. We then turn to a necessary component of that autonomy: information. We review data from a recent survey indicating litigants felt confused and uninformed regarding their suits. In light of that evidence, we assess what transferee courts are doing to keep litigants up-to-date and well informed. We then furnish the results of our own empirical analysis of court-run MDL websites, which are often extolled, including by judges, as a key venue for client-court communication. Unfortunately, our analysis reveals deep and pervasive deficits with respect to usability and relevance. If this is where case-related communication is supposed to be happening, then litigant confusion is unsurprising. We close with recommendations for courts seeking to harness simple technology to promote better communication. Improved MDL websites aren’t a panacea. But they might promote the autonomy interests of litigants—and light a path for future reform.

Court-connected ODR has already shown itself capable of dramatically improving access to justice by eliminating barriers rooted in the fact that courts traditionally resolve disputes only during certain hours, in particular physical places, and only through face-to-face proceedings. Given the centrality of courthouses to our system of justice, too many Americans have discovered their rights are too difficult or costly to exercise. As court-connected ODR systems spread, offering more inclusive types of dispute resolution services, people will soon find themselves with the law and the courts at their fingertips. But robust access to justice requires more than just raw, low-cost opportunities to resolve disputes. Existing ODR platforms seek to replicate in-person procedures, simplifying and clarifying steps where possible, but litigants without representation still proceed without experience, expertise, guardrails, or the ability to gauge risk or likely outcomes. Injecting ODR with a dose of data science has the potential to address many of these shortfalls. Enhanced ODR is unlikely to render representation obsolete, but it can dramatically reduce the gap between the “haves” and the “have nots” and, on some dimensions—where machines can outperform humans—next generation platforms may be a significant improvement.

The legal services market is commonly thought of as divided into two “hemispheres”—PeopleLaw, which serves individuals and small businesses, and BigLaw, which serves corporated clients. The last few decades have seen an increasing concentration of resources within the legal profession toward the latter, to the alleged detriment of the former. At the same time, the costs of accessing legal representation exceed the financial resources of many ordinary citizens and small businesses, compromising their access to the legal system. We ask: Will the adoption of new digital technologies lead to a levelling of the playing field between the PeopleLaw and BigLaw sectors? We consider this in three related dimensions. First, for users of legal services: Will technology deliver reductions in cost sufficient to enable affordable access to the legal system for consumer clients whose legal needs are currently unmet? Second, for legal services firms: Will the deployment of technology to capture economies of scale mean that firms delivering legal services across the two segments become more similar? And third, for the structure of the legal services market: Will the pursuit of economies of scale trigger consolidation that leads both segments toward a more concentrated market structure?

Natural language processing techniques promise to automate an activity that lies at the core of many tasks performed by lawyers, namely the extraction and processing of information from unstructured text. The relevant methods are thought to be a key ingredient for both current and future legal tech applications. This chapter provides a non-technical overview of the current state of NLP techniques, focusing on their promise and potential pitfalls in the context of legal tech applications. It argues that, while NLP-powered legal tech can be expected to outperform humans in specific categories of tasks that play to the strengths of current ML techniques, there are severe obstacles to deploying these tools in other contexts, most importantly in tasks that require the equivalent of legal reasoning.

Compared to other highly skilled labour markets, the legal profession has been slow to adopt technological changes, but lawyers have been downright speedy compared to courts. Courts already generate voluminous amounts of data, but restrict access to it. The private sector, recognizing how legal data can help its clients, willingly incurs the costs to gather this data and develop proprietary tools to analyze it. As this trend continues, access to justice will worsen, further benefitting wealthier clients over opposing litigants as well as the courts, for no other reason than that courts will lag in their ability to efficaciously decide judicial matters. The judiciary—across all levels—could reverse this trend. The first step is to develop a more robust institutional framework for making court data publicly available. The second step is a willingness among courts to analyze this data when issuing decisions, both procedural and sustantive. Doing so requires courts to develop core competencies in existing and emerging legal technology. Democratizing access to judicial data will diminish the advantage currently enjoyed by affluent litigants, but accrue to the benefit of everyone else—including courts themselves.

Although proponents of online dispute resolution systems proclaim that their innovations will expand access to justice for so-called “simple cases,” evidence of how the technology actually operates and who is benefitting from it demonstrates just the opposite. Resolution of some disputes may be more expeditious and user interface more intuitive. But in order to achieve this, parties generally do not receive meaningful information about their rights and defenses. The opacity of the technology (ODR code is not public and unlike court appearance its proceedings are private) means that due process defects and systemic biases are difficult to identify and address. Worse still, the “simple cases” argument for ODR assumes that the dollar value of a dispute is a reasonable proxy for its complexity and significance to the parties. This assumption is contradicted by well established research on procedural justice. Moreover, recent empirical studies show that low money value cases, which dominate state court dockets, are for the most part debt collection proceedings brought by well-represented private creditors or public creditors (including courts themselves, which increasingly depend on fines and fees for their operating budget). Defendants in these proceedings are overwhelmingly unrepresented individuals. What ODR offers in these settings is not access to justice for ordinary people, but rather a powerful accelerated collection and compliance technology for private creditors and the state. This chapter examines the design features of ODR and connects them to the ideology of tech evangelism that drives deregulation and market capture, the aspirations of the alternative dispute resolution movement, and hostility to the adversary system that has made strange bedfellows of traditional proponents of access to justice and tech profiteers. The chapter closes with an analysis of front-end standards for courts and bar regulators to consider to ensure that technology marketed in the name of access to justice actually serves the legal needs of ordinary people.

America’s market for legal technology presents a puzzle. On the one hand, America’s market for legal services is among the most tightly regulated in the world, suggesting infertile ground for a legal technology revolution. On the other side of this puzzle is America’s advanced and free-wheeling market for legal tech, which is likely the most robust in the world. This chapter explains this seeming puzzle and then uses that explanation to make some predictions about where legal technology will continue to flourish in America and where legacy players—lawyers, law schools, and judges—will instead stymie its development. In order to predict the future we first must understand the present and the past, so the chapter presents a brief overview of lawyer regulation, the structure of the American market for lawyers and legal services, and the current state of legal tech. This more granular view of the innovation ecosystem can explain why some tech sectors are booming, while others remain stubbornly behind, and also where we’ll see continued and even accelerated legal tech growth and where we won’t.

Should the justice system sustain remote operations in a post-pandemic world? Commentators are skeptical, particularly regarding online jury trials. Some of this skepticism stems from empirical concerns. This paper explores two oft-expressed concerns for sustaining remote jury trials: first, that using video as a communication medium will dehumanize parties to a case, reducing the human connection from in-person interactions and making way for less humane decision-making; and second, that video trials will diminish the ability of jurors to detect witness deception or mistake. Our review of relevant literature suggests that both concerns are likely misplaced. Although there is reason to exercise caution and to include strong evaluation with any migration online, available research suggests that video will neither materially affect juror perceptions of parties nor alter the jurors’ (nearly nonexistent) ability to discern truthful from deceptive or mistaken testimony. On the first point, the most credible studies from the most analogous situations suggest video interactions cause little or no effect on human decisions. On the second point, a well-developed body of social science research shows a consensus that human detection accuracy is only slightly above chance levels, and that such accuracy is the same whether the interaction is in person or virtual.