The philosophy of law is the study of the nature of law: What is law? What are the criteria of a functioning legal system? What is the relationship between law and morality? A course on legal technology and legal informatics focuses on the technological implementation of a legal system. As we move away from static, printed documents toward virtual, distributed, integrated systems, software (“code”) plays an increasingly important role in the legal system. Obviously code has applications far beyond implementing law. Yet, as Lawrence Lessig points out in Code 2.0, non-law code also regulates behavior, often in a more fundamental way than laws do.1 As discussed in this chapter, code is architectural in nature and effectively limits behavior similarly to laws of physics. Only in science fiction do we entertain the notion of faster-than-light travel, perpetual motion, or evading gravitational forces. Similarly, we tend to accept the limitations of code that prevent us from, say, lending out our e-books, though such lending would certainly be legal. Even if we are aware of these limits and do not like them, most of us have no capacity to change them. As far as our behavior is concerned, code may as well be the law.2

Almost all law is expressed in natural language; therefore, natural language processing (NLP) is a key component of understanding and predicting law. Natural language processing converts unstructured text into a formal representation that computers can understand and analyze. This technology has already intersected with law, and is poised to experience rapid innovation and widespread adoption. There are three reasons for this: (1) the number of repositories of digitized machine-readable legal text data is growing; (2) advances in NLP tools are being driven by algorithmic and hardware improvements; and (3) there is great potential to dramatically improve the effectiveness of legal services due to inefficiencies in its current practice.

This case study describes how a team of computer scientists assisted a team of public health researchers by applying machine learning to extract information from statutory texts. Researchers at the University of Pittsburgh’s Graduate School of Public Health (SPH) had been manually mining specific information from federal, state, and local laws and regulations concerning public health system emergency preparedness and response. The analysts used the information to assess and compare states’ regulatory frameworks concerning emergency preparedness. They retrieved candidate legal and regulatory texts from a full-text legal information service, identified relevant spans of text, and systematically categorized the spans in terms of a coding scheme. The SPH’s coding scheme captured information about agencies and actors in a state’s public health system who were directed by statute to interact with one another in particular ways while dealing with public health emergencies. Based on the coded information, the SPH constructed statutory network diagrams of legally mandated interactions among actors. These network diagrams provide insight into those statutory texts that directed the interactions.

There are a variety of different approaches to representing legal information in order to make that information usable in legal analytics or automated reasoning systems. This variety of approaches stems from the variety of tasks that legal practitioners wish to undertake, and there is no single representation technique that squarely fits all tasks and contexts. This chapter will survey the methods available for representing legal information, and the benefits that each method can bring to the tools used in legal work. This section will also provide a high-level overview of approaches that have been developed to represent laws expressed as statutes, rules, and regulations. It will also survey the standards that have been developed and that are emerging for representing legal information. An overview of methods for representing case law will also be provided, along with a summary of how interpretation of the law can be achieved through automated reasoning mechanisms based on computational models of argument. This chapter will also consider methods of conceptualizing and reasoning about the relations within and between legal documents such as contracts, along with techniques to represent wider networks of information, such as document citations.

Every once in a while, a ready-to-use data set falls down the chimney like a diamond in a gift box, perfectly suited to the problem at hand. Unfortunately, what we usually have is a pile of coal and a rusty shovel. This is because most data resides in unstructured and disparate information systems or data sources. In order to apply most informatics methods, including a markup system like XML, we must first retrieve and then preprocess data from these sources to produce a structured, linked data set. These phases, sometimes colloquially referred to as data scraping, cleaning, wrangling, or “munging,” are arguably more important, and typically more time-consuming, than many other tasks in legal informatics.

Gamification refers to the use of game mechanics merged with behavioral analytics in a non-game setting.1 Gamification is used to improve production and performance in the workplace by engaging the user to behave in a way that is aligned with the goals of the business. Gamification occurs when a process, such as entering billable hours into the firm’s software or filling out an online client intake form, is mixed with game elements in such a way that firm members are motivated to complete tasks in a more desirable way. Businesses have used gamification strategies, with differing levels of sophistication, on issues including customer relationship management, training, market research, business intelligence, and education. Other professions, many in health care, are now also turning to gamification to increase engagement in a number of workplace processes for both their staff, and the clients they serve.

As highlighted in , the field of artificial intelligence (AI) is broad and embraces a wide range of approaches. While it is of course possible to imagine the linkages between topics such as machine vision and robotics to the business, practice, and delivery of law, the two most relevant topics within the AI landscape are natural language processing (NLP) (discussed in Chapter 2.8) and machine learning (ML).

Big Law has been described as being in the throes of a painful transformation brought about by factors such as globalization, the increased use of technology, and a transition from a supply-driven market to a demand-driven one. A common framework for such upheaval is Clayton Christensen’s The Innovator’s Dilemma, which generally portends an inevitable collapse of market incumbents when they cater to the performance requirements of their high-value customers’ demands. Big Law is not immune to the principles of The Innovator’s Dilemma. However, neither the disruptive nor sustaining innovation described in Christensen’s work seem to adequately characterize the changes occurring. In this chapter, we describe a hybrid model, adaptive innovation, that takes into account the opposing forces in play. As with most other sectors, lawyers have argued that Big Law is different. This chapter reviews some of the most cited factors predicting and denying the demise of Big Law. We argue that market-imposed values such as quality, efficiency, and ROI will likely dominate over reputation and comprehensiveness, forcing a fundamental change in many common features of Big Law. However, law firms will likely remain an inevitable mechanism for the delivery of services, albeit under a different model.

From document review in litigation, to compliance, case prediction, billing, negotiation and settlement, contracting, patent management, due diligence, legal research, and beyond, technology is transforming the production of legal work and in turn the economics of the legal industry. Legal informatics is the academic discipline that underlies many of these transformational technologies, and despite all of these technical advances, no modern comprehensive treatment of the field has been offered to date. With contributions from more than two dozen academic and industry experts, this book offers readers a first-of-its-kind introductory overview of the exciting field of legal informatics.

If an event of interest is correlated with text data, we can learn models of text that predict the event outcome. For example, researchers have predicted financial risk with regression models that use the text of company financial disclosures.2 Topic models can predict outcomes as a function of the proportions of a document that are devoted to the automatically discovered topics,3 and this technique has been used to develop, for example, a topic model that forecasts roll call votes using the text of congressional bills.4 An advantage of the topic model prediction approach is that the model learns interpretable topics and the relationships between the learned topics and outcomes. A disadvantage of the topic model approach is that other, less interpretable text models often exhibit higher predictive power.

Electronic discovery (e-discovery) is an integral component of legal informatics, touching on everything from search and artificial intelligence to design and legal services transformation. Any discussion of electronic discovery must begin with an explanation of its relevance to legal work. E-discovery – also known as “ediscovery” or, somewhat datedly, “eDiscovery” – is the discovery in legal proceedings of evidence in an electronic format. Due to the nature of modern technology, e-discovery encompasses an overwhelming majority of evidence, such that e-discovery and other forms of discovery have become virtually synonymous. As such, legal discovery is now fraught with issues concerning how information is stored, retrieved, exchanged, and generally made accessible to parties during legal proceedings. A common challenge for attorneys is what to do with a multi-terabyte collection of evidence that consists of millions of documents across hundreds of file types, with only a matter of months before their first depositions. The best solutions to this kind of increasingly common challenge will include recourse to big data and machine learning, which are discussed in this chapter.

The world of contracts is undergoing fundamental changes. This is partly due to technology: there can be tremendous benefits from self-enforcing, machine-readable contracts. But these technologies are not used everywhere. Many contracts continue to be performed by people. In the context of commercial deals and relationships,1 a vast number of contracts still need to be planned, understood, approved, implemented, and monitored by people.2 Initiatives across the world seek to innovate contracting processes and documents and develop more effective, engaging ways for people to work with them. This chapter focuses on these initiatives and the need to make contracts truly human-readable.

This case study demonstrates in more detail how one particular technique – computational argumentation – can be effectively used to build automated reasoning tools that provide decision support capabilities for legal practitioners. This case study will also demonstrate how legal cases can be represented and interpreted through computational models of arguments, and how this enables software programs to generate and reason about the relevant arguments for deciding a case, akin to human judicial reasoning.