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?

We set out the case for computational social science as opposed to traditional “pencil and paper” formal methods. The substantive theme of this book is the governance cycle in parliamentary democracies, but the ideas we put forward can be applied to many other areas of study.

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.

We set out an alternative, “top down”, approach to agent-based modeling. We develop an artificial intelligence (AI) algorithm to navigate the governance cycle using what we can think of as computational game theory. AI models have had formidable success in solving games like Chess, Go, and especially a bluffing game like Poker, suggesting they also have the potential to attack difficult political games. Addressing a simplified version of the government formation process as a noncooperative game, the AI algorithm deploys Monte Carlo Counterfactual Regret (MCCFR). During in massively repeated self-play, it samples paths though the vast game tree to relentlessly learn near optimal strategies.

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.

While heavy-duty computational methods have revolutionized much empirical work in political science, computational analysis has yet to have much any impact on theoretical accounts of politics – in contrast to the situation in many of the natural sciences. We set here out to map a path forward in computational social science. Analyzing the complex and deductively intractable “governance cycle” that plays out in the high-dimensional issue spaces of parliamentary systems, we use two different computational approaches. One models functionally rational politicians who deploy rules of thumb to navigate their complex environment. The other deploys an artificial intelligence algorithm which systematic learns, from massively repeated self-play, to find near-optimal strategies. Future work made possible by greater computational firepower would enable better AI, more realistic ABMs, and the modeling of logrolling under the conditions of incomplete information which characterize most real-world bargaining and negotiation.

What effect will potent new legal tech tools have on the civil litigation landscape, and what can or should we do about it? Recent trends in plaintiff win rates and damages awards suggest the American civil justice system is growing more slanted toward the “haves” at the expense of the “have-nots.” Some say that AI-fired legal tech tools will reverse this trend and democratize the system. We disagree. Potent new legal tech tools are surely coming. Many are already here. But these tools are, and will likely continue to be, unevenly distributed because of the privileged access to data and technical know-how of emerging consortia of corporations, law firms, and tech companies. As a result, legal tech will, at least over the near- to medium-term, further skew the litigation playing field, shaping not just the resolution of claims but also the evolution of substantive law. As the American civil justice system enters the digital age, the haves will be propelled yet further ahead.

We outline the core argument of the book and steps taken to establish this. We begin by sketching component parts of the governance cycle: election, government formation, and government survival. Noting that the analysis of this complex system is intractable for traditions deductive methods of formal modeling, we preview two different computational methods for analyzing it. First, we model “functionally rational” artificial agents who use simple but effective rules of thumbs to navigate their high stakes but complex environment (ABM). Second, we specify an artificial intelligence (AI) algorithm which, by massively repeated self-play, teaches itself to find near-optimal strategies for playing what is in effect a traditional, but intractable, noncooperative game. We conclude by sketching the empirical approach we use to first calibrate and exercise the models on training data and then test them on out-of-sample test data.

Chapter 10 looks at out of control ultra-technologies that can wreck the human future and what we can do about them.