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.

Companies, and the professionals who serve them, spend vast amounts of time extracting data from contracts. This work is done in areas including M&A due diligence and integration, corporate contract management, lease abstraction, auditing, and others. In recent years, software has come to market that helps users review contracts faster and more accurately, and that also helps to better organize the process and understand its results.

There are a variety of informatics-centric tasks for which the goal is to predict something or extract some kind of signal. In this section, we consider artificial intelligence broadly, artificial intelligence applied to law, and the very fruitful fields of machine learning (ML) and natural language processing (NLP).

The General Counsel of a Fortune 100 company was recently asked if he measured ROI (return on investment) on his legal spend. “No,” he said, “I can’t. I can’t measure quality.”

At various conference panels, several of the largest firms claim they are revamping the way they handle their legal spend to be more in line with other cost centers.1 The rise of “Legal Operations” in corporate legal departments is leading the way in the use of legal metrics.2 These standard business metrics include performance, efficiency, and value. ROI means measuring return, and return requires estimating value. Value can be defined as quality divided by cost. Therefore, measuring quality is key to the modernization of legal departments, as well as their external legal service providers.