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.

In October 2017, Alphabet and the Government of Canada announced a joint effort: the first smart city powered by Alphabet’s technology. The smart city was proposed to be built in Toronto, Canada, where Alphabet’s subsidiary Sidewalk Labs had partnered with public corporation Waterfront Toronto. Balancing public, private, and collective interests in smart cities is a challenging task, that is why Sidewalk Labs proposed some innovative instruments of governance and management in their city infrastructure. This chapter draws on the GKC framework to examine the company’s proposal for the governance of smart infrastructure. The analysis focuses on two action arenas: data-driven planning and the trusts.

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?

Smart cities require trusted governance and engaged citizens, especially governance of intelligence and intelligence-enabled control. In some very important respects, smart cities should remain dumb, and that will take governance. This introduction provides an overview of the book’s aims, structure, and contributions of individual chapters.

This case study focuses on smart tech deployment and governance in Philadelphia. In 2019, the City of Philadelphia launched a new smart city initiative, SmartCityPHL. SmartCityPHL includes a roadmap of strategies, processes, and plans for deployment. In many ways, the new initiative is remarkable. It is ambitious yet pragmatic; it outlines a set of guiding principles along with deliberative and participatory processes; it is broadly inclusive of people and values – as reflected in its simple definition of a smart city: “a city that uses integrated information and communication technology to support the economic, social, and environmental goals of its community.” On its face, and perhaps in comparison with other smart city initiatives, SmartCityPHL provides an exciting roadmap. But the 2019 initiative was not the first smart city project in Philadelphia. There is, in fact, a long history of Philadelphians turning to supposedly smart technology to solve community problems.

This chapter outlines a forward-looking, intelligent approach to thinking through and evaluating supposedly smart systems. First, it clarifies that it is not the city that is smart. Rather, smartness is better understood and evaluated in terms of affordances supposedly smart tools provide actual people. Who gains what kinds of intelligence? For what purposes? Subject to what governance? Second, it identifies and addresses key challenges to intelligent governance in smart city projects. Cities must move beyond a transactional mindset, appreciate how smart systems become an integral part of the built environment, and develop appropriate governance. Third, it proposes an approach to smart city governance grounded in local, contextual norms and scaffolded by key questions to ask throughout smart city planning, procurement, implementation, and management processes. This approach is importantly not oriented around Elinor Ostrom’s famous design principles, but rather a shared set of evaluative questions to guide decision-making.

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.