Law Schools as Knowledge Centers in the Digital Age

This article explores what it would mean for law schools to be “knowledge centers” in the digital age, and to have this as a central mission. It describes the activities of legal knowledge centers as: (1) focusing on solving real legal problems in society outside of the academy; (2) evaluating the problem-solving effectiveness of the legal knowledge being developed; (3) re-conceptualizing the structures used to represent legal knowledge, the processes through which legal knowledge is created, and the methods used to apply that knowledge; and (4) disseminating legal knowledge in ways that assist its implementation. The Article uses as extended examples of knowledge centers in the digital age the research laboratories in the sciences, and in particular research laboratories in linguistics and information science. It uses numerous examples to suggest how law schools might implement the concept of a knowledge center

Law Schools as Knowledge Centers in the Digital Age

This article explores what it would mean for law schools to be “knowledge centers” in the digital age, and to have this as a central mission. It describes the activities of legal knowledge centers as: (1) focusing on solving real legal problems in society outside of the academy; (2) evaluating the problem-solving effectiveness of the legal knowledge being developed; (3) re-conceptualizing the structures used to represent legal knowledge, the processes through which legal knowledge is created, and the methods used to apply that knowledge; and (4) disseminating legal knowledge in ways that assist its implementation. The Article uses as extended examples of knowledge centers in the digital age the research laboratories in the sciences, and in particular research laboratories in linguistics and information science. It uses numerous examples to suggest how law schools might implement the concept of a knowledge center

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

The Intuitive Appeal of Explainable Machines

Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties. Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible. In most cases, intuition serves as the unacknowledged bridge between a descriptive account and a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself

LAW SEARCH IN THE AGE OF THE ALGORITHM

The process of searching for relevant legal materials is fundamental to legal reasoning. However, despite its enormous practical and theoretical importance, law search has not been given significant attention by scholars. In this Article, we define the problem of law search and examine the consequences of new technologies capable of automating this core lawyerly task. We introduce a theory of law search in which legal relevance is a sociological phenomenon that leads to convergence over a shared set of legal materials and explore the normative stakes of law search. We examine ways in which law scholars can understand empirically the phenomenon of law search, argue that computational modeling is a valuable epistemic tool in this domain, and report the results from a multi-year, interdisciplinary effort to develop an advanced law search algorithm based on human-generated data. Finally, we explore how policymakers can manage the challenges posed by new machine learning-based search technologies

Information for Impact: Liberating Nonprofit Sector Data

This paper explores the costs and benefits of four avenues for achieving open Form 990 data: a mandate for e-filing, an IRS initiative to turn Form 990 data into open data, a third-party platform that would create an open database for Form 990 data, and a priori electronic filing. Sections also discuss the life and usage of 990 data. With bibliographical references

The Coming Transformative Impact of Large Language Models and Artificial Intelligence on Global Business and Education

Rapid advances in the capabilities of Large Language Models (LLM) as a basis for Artificial Intelligence (AI) applications, and their sudden wide accessibility, have garnered significant attention recently. These technologies (e.g., ChatGPT, BARD), which have the ability to predict and generate human language, have led to excitement and concerns regarding their use in various industries. This paper explores the history of LLM, examines their applications in business and education, and delves into the critical ethical concerns and challenges of these emerging technologies to ensure that their uses are not only effective, but also responsible and equitable

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)