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| data science in law: Data-Driven Law Edward J. Walters, 2018-07-16 For increasingly data-savvy clients, lawyers can no longer give it depends answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions. Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as: Mining legal data Computational law Uncovering bias through the use of Big Data Quantifying the quality of legal services Data mining and decision-making Contract analytics and contract standards In addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable. Data-driven decision-making requires firms to think differently about their workflow. Most firms warehouse their files, never to be seen again after the matter closes. Running a data-driven firm requires lawyers and their teams to treat information about the work as part of the service, and to collect, standardize, and analyze matter data from cradle to grave. More than anything, using data in a law practice requires a different mindset about the value of this information. This book helps legal professionals to develop this data-driven mindset. |
| data science in law: Algorithmic Governance and Governance of Algorithms Martin Ebers, Marta Cantero Gamito, 2021 Algorithms are now widely employed to make decisions that have increasingly far-reaching impacts on individuals and society as a whole (algorithmic governance), which could potentially lead to manipulation, biases, censorship, social discrimination, violations of privacy, property rights, and more. This has sparked a global debate on how to regulate AI and robotics (governance of algorithms). This book discusses both of these key aspects: the impact of algorithms, and the possibilities for future regulation. |
| data science in law: Research Handbook in Data Science and Law Vanessa Mak, Eric Tjong Tjin Tai, Anna Berlee, 2018 The use of data in society has seen an exponential growth in recent years. Data science, the field of research concerned with understanding and analyzing data, aims to find ways to operationalize data so that it can be beneficially used in society, for example in health applications, urban governance or smart household devices. The legal questions that accompany the rise of new, data-driven technologies however are underexplored. This book is the first volume that seeks to map the legal implications of the emergence of data science. It discusses the possibilities and limitations imposed by the current legal framework, considers whether regulation is needed to respond to problems raised by data science, and which ethical problems occur in relation to the use of data. It also considers the emergence of Data Science and Law as a new legal discipline. |
| data science in law: Artificial Intelligence and Legal Analytics Kevin D. Ashley, 2017-07-10 This book describes how text analytics and computational models of legal reasoning will improve legal IR and let computers help humans solve legal problems. |
| data science in law: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science in law: Research Handbook in Data Science and Law Vanessa Mak, Eric Tjong Tjin Tai, Anna Berlee, 2024-08-06 This thoroughly updated Research Handbook examines the recent exponential growth of data use in society and its implications for legal research and practice. It explores contemporary research in the field of data science, as well as the operationalization of data for use in healthcare, urban governance and smart household devices, among others. |
| data science in law: Law, Computer Science, and Artificial Intelligence Ajit Narayanan, Mervyn Bennun, 1998 This text examines the interaction between the disciplines of law, computer science and artificial intelligence. The chapters are grouped into theory, implications and applications sections, in an attempt to identify separate, but interrelated methodological stances |
| data science in law: Data Science John D. Kelleher, Brendan Tierney, 2018-04-13 A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects. |
| data science in law: Law for Computer Scientists and Other Folk Mireille Hildebrandt, 2020 This book introduces law to computer scientists and other folk. Computer scientists develop, protect, and maintain computing systems in the broad sense of that term, whether hardware (a smartphone, a driverless car, a smart energy meter, a laptop, or a server), software (a program, an application programming interface or API, a module, code), or data (captured via cookies, sensors, APIs, or manual input). Computer scientists may be focused on security (e.g. cryptography), or on embedded systems (e.g. the Internet of Things), or on data science (e.g. machine learning). They may be closer to mathematicians or to electrical or electronic engineers, or they may work on the cusp of hardware and software, mathematical proofs and empirical testing. This book conveys the internal logic of legal practice, offering a hands-on introduction to the relevant domains of law, while firmly grounded in legal theory. It bridges the gap between two scientific practices, by presenting a coherent picture of the grammar and vocabulary of law and the rule of law, geared to those with no wish to become lawyers but nevertheless required to consider the salience of legal rights and obligations. Simultaneously, this book will help lawyers to review their own trade. It is a volume on law in an onlife world, presenting a grounded argument of what law does (speech act theory), how it emerged in the context of printed text (philosophy of technology), and how it confronts its new, data-driven environment. Book jacket. |
| data science in law: Research Handbook in Data Science and Law Vanessa Mak, Eric Tjong Tjin Tai, Anna Berlee, 2018-12-28 The use of data in society has seen an exponential growth in recent years. Data science, the field of research concerned with understanding and analyzing data, aims to find ways to operationalize data so that it can be beneficially used in society, for example in health applications, urban governance or smart household devices. The legal questions that accompany the rise of new, data-driven technologies however are underexplored. This book is the first volume that seeks to map the legal implications of the emergence of data science. It discusses the possibilities and limitations imposed by the current legal framework, considers whether regulation is needed to respond to problems raised by data science, and which ethical problems occur in relation to the use of data. It also considers the emergence of Data Science and Law as a new legal discipline. |
| data science in law: Legal Challenges of Big Data Joe Cannataci, Valeria Falce, Oreste Pollicino, 2020-09-25 This groundbreaking book explores the new legal and economic challenges triggered by big data, and analyses the interactions among and between intellectual property, competition law, free speech, privacy and other fundamental rights vis-à-vis big data analysis and algorithms. |
| data science in law: Judicial Decision-Making Barry Friedman, Margaret H. Lemos, Andrew D. Martin, Tom S. Clark, Allison Orr Larsen, Anna Harvey, 2020-04-27 This book is the only comprehensive treatment of judicial decision-making that combines social science with a sophisticated understanding of law and legal institutions. It is designed for everyone from undergraduates to law students and graduate students. Topics include whether the identity of the judge matters in deciding a case, how different types of lawyers and litigants shape the work of judges, how judges follow or defy the decisions of higher courts, how judges bargain with one another on multi-member courts, how judges get and keep their jobs, and how the judicial branch interacts with the other branches of government and the general public. The book explains how these individual and institutional features affect who wins and loses cases, and how the law itself is changed. It is built around well-known and accessible disputes such as gay marriage, women's rights, Obamacare, and the death penalty; and it offers students a new way to think about familiar legal issues and demonstrates how legal and social-science perspectives can produce a better understanding of courts and judges. |
| data science in law: Legal Informatics Daniel Martin Katz, Ron Dolin, Michael J. Bommarito, 2021-02-18 This cutting-edge volume offers a theoretical and applied introduction to the emerging legal technology and informatics industry. |
| data science in law: Legal Data and Information in Practice Sarah A. Sutherland, 2022-01-31 Legal Data and Information in Practice provides readers with an understanding of how to facilitate the acquisition, management, and use of legal data in organizations such as libraries, courts, governments, universities, and start-ups. Presenting a synthesis of information about legal data that will furnish readers with a thorough understanding of the topic, the book also explains why it is becoming crucial that data analysis be integrated into decision-making in the legal space. Legal organizations are looking at how to develop data-driven insights for a variety of purposes and it is, as Sutherland shows, vital that they have the necessary skills to facilitate this work. This book will assist in this endeavour by providing an international perspective on the issues affecting access to legal data and clearly describing methods of obtaining and evaluating it. Sutherland also incorporates advice about how to critically approach data analysis. Legal Data and Information in Practice will be essential reading for those in the law library community who are based in English-speaking countries with a common law tradition. The book will also be useful to those with a general interest in legal data, including students, academics engaged in the study of information science and law. |
| data science in law: Law as Data Michael A. Livermore, Daniel N. Rockmore, 2018-12 In recent years, the digitization of legal texts and developments in the fields of statistics, computer science, and data analytics have opened entirely new approaches to the study of law. This volume explores the new field of computational legal analysis, an approach marked by its use of legal texts as data. The emphasis herein is work that pushes methodological boundaries, either by using new tools to study longstanding questions within legal studies or by identifying new questions in response to developments in data availability and analysis. By using the text and underlying data of legal documents as the direct objects of quantitative statistical analysis, Law as Data introduces the legal world to the broad range of computational tools already proving themselves relevant to law scholarship and practice, and highlights the early steps in what promises to be an exciting new approach to studying the law. |
| data science in law: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science in law: New Technologies for Human Rights Law and Practice Molly K. Land, Jay D. Aronson, 2018-04-19 New technological innovations offer significant opportunities to promote and protect human rights. At the same time, they also pose undeniable risks. In some areas, they may even be changing what we mean by human rights. The fact that new technologies are often privately controlled raises further questions about accountability and transparency and the role of human rights in regulating these actors. This volume - edited by Molly K. Land and Jay D. Aronson - provides an essential roadmap for understanding the relationship between technology and human rights law and practice. It offers cutting-edge analysis and practical strategies in contexts as diverse as autonomous lethal weapons, climate change technology, the Internet and social media, and water meters. This title is also available as Open Access. |
| data science in law: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science in law: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| data science in law: Computational Legal Studies Ryan Whalen, 2020-09-25 Featuring contributions from a diverse set of experts, this thought-provoking book offers a visionary introduction to the computational turn in law and the resulting emergence of the computational legal studies field. It explores how computational data creation, collection, and analysis techniques are transforming the way in which we comprehend and study the law, and the implications that this has for the future of legal studies. |
| data science in law: Life and the Law in the Era of Data-Driven Agency Mireille Hildebrandt, Kieron O’Hara, 2020-01-31 This ground-breaking and timely book explores how big data, artificial intelligence and algorithms are creating new types of agency, and the impact that this is having on our lives and the rule of law. Addressing the issues in a thoughtful, cross-disciplinary manner, leading scholars in law, philosophy, computer science and politics examine the ways in which data-driven agency is transforming democratic practices and the meaning of individual choice. |
| data science in law: Runaway Technology Joshua A. T. Fairfield, 2021-02-25 Law can keep up with rapid technological change by reflecting our evolving understanding of how humans use language to cooperate. |
| data science in law: Human Law and Computer Law: Comparative Perspectives Mireille Hildebrandt, Jeanne Gaakeer, 2013-05-23 The focus of this book is on the epistemological and hermeneutic implications of data science and artificial intelligence for democracy and the Rule of Law. How do the normative effects of automated decision systems or the interventions of robotic fellow ‘beings’ compare to the legal effect of written and unwritten law? To investigate these questions the book brings together two disciplinary perspectives rarely combined within the framework of one volume. One starts from the perspective of ‘code and law’ and the other develops from the domain of ‘law and literature’. Integrating original analyses of relevant novels or films, the authors discuss how computational technologies challenge traditional forms of legal thought and affect the regulation of human behavior. Thus, pertinent questions are raised about the theoretical assumptions underlying both scientific and legal practice. |
| data science in law: Data Science Ivo D. Dinov, Milen Velchev Velev, 2021-12-06 The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the problems of time. The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public. |
| data science in law: Elgar Encyclopedia of Law and Data Science Comandé, Giovanni, 2022-02-18 This Encyclopedia brings together jurists, computer scientists, and data analysts to map the emerging field of data science and law for the first time, uncovering the challenges, opportunities, and fault lines that arise as these groups are increasingly thrown together by expanding attempts to regulate and adapt to a data-driven world. It explains the concepts and tools at the crossroads of the many disciplines involved in data science and law, bridging scientific and applied domains. Entries span algorithmic fairness, consent, data protection, ethics, healthcare, machine learning, patents, surveillance, transparency and vulnerability. |
| data science in law: Data Science for Entrepreneurship Werner Liebregts, Willem-Jan van den Heuvel, Arjan van den Born, 2023-03-23 The fast-paced technological development and the plethora of data create numerous opportunities waiting to be exploited by entrepreneurs. This book provides a detailed, yet practical, introduction to the fundamental principles of data science and how entrepreneurs and would-be entrepreneurs can take advantage of it. It walks the reader through sections on data engineering, and data analytics as well as sections on data entrepreneurship and data use in relation to society. The book also offers ways to close the research and practice gaps between data science and entrepreneurship. By having read this book, students of entrepreneurship courses will be better able to commercialize data-driven ideas that may be solutions to real-life problems. Chapters contain detailed examples and cases for a better understanding. Discussion points or questions at the end of each chapter help to deeply reflect on the learning material. |
| data science in law: Machine Learning Forensics for Law Enforcement, Security, and Intelligence Jesus Mena, 2016-04-19 Increasingly, crimes and fraud are digital in nature, occurring at breakneck speed and encompassing large volumes of data. To combat this unlawful activity, knowledge about the use of machine learning technology and software is critical. Machine Learning Forensics for Law Enforcement, Security, and Intelligence integrates an assortment of deductive |
| data science in law: Algorithms and Law Martin Ebers, Susana Navas, 2020-07-23 Exploring issues from big-data to robotics, this volume is the first to comprehensively examine the regulatory implications of AI technology. |
| data science in law: Big Data, Health Law, and Bioethics I. Glenn Cohen, Holly Fernandez Lynch, Effy Vayena, Urs Gasser, 2018-03-08 When data from all aspects of our lives can be relevant to our health - from our habits at the grocery store and our Google searches to our FitBit data and our medical records - can we really differentiate between big data and health big data? Will health big data be used for good, such as to improve drug safety, or ill, as in insurance discrimination? Will it disrupt health care (and the health care system) as we know it? Will it be possible to protect our health privacy? What barriers will there be to collecting and utilizing health big data? What role should law play, and what ethical concerns may arise? This timely, groundbreaking volume explores these questions and more from a variety of perspectives, examining how law promotes or discourages the use of big data in the health care sphere, and also what we can learn from other sectors. |
| data science in law: Science at the Bar Sheila Jasanoff, 1997-09-30 Issues spawned by the headlong pace of developments in science and technology fill the courts. The realm of the law is sometimes at a loss—constrained by its own assumptions and practices, Jasanoff suggests. This book exposes American law’s long-standing involvement in constructing, propagating, and perpetuating myths about science and technology. |
| data science in law: Advancing Innovation and Sustainable Outcomes in International Graduate Education Mohan Raj Gurubatham, Geoffrey Alan Williams, 2020-07 This book raises awareness of the global challenges posed by accelerating global drivers for graduate education in the 21st century. It also evaluates the impacts of the 4th Industrial Revolution and its impacts on skill sets and high value graduate education-- |
| data science in law: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| data science in law: The Oxford Handbook of Empirical Legal Research Peter Cane, Herbert Kritzer, 2012-05-17 The empirical study of law, legal systems and legal institutions is widely viewed as one of the most exciting and important intellectual developments in the modern history of legal research. Motivated by a conviction that legal phenomena can and should be understood not only in normative terms but also as social practices of political, economic and ethical significance, empirical legal researchers have used quantitative and qualitative methods to illuminate many aspects of law's meaning, operation and impact. In the 43 chapters of The Oxford Handbook of Empirical Legal Research leading scholars provide accessible and original discussions of the history, aims and methods of empirical research about law, as well as its achievements and potential. The Handbook has three parts. The first deals with the development and institutional context of empirical legal research. The second - and largest - part consists of critical accounts of empirical research on many aspects of the legal world - on criminal law, civil law, public law, regulatory law and international law; on lawyers, judicial institutions, legal procedures and evidence; and on legal pluralism and the public understanding of law. The third part introduces readers to the methods of empirical research, and its place in the law school curriculum. |
| data science in law: Automated Vehicle Law Jeffrey K. Gurney, 2020-11 This book is framed around five areas of automated vehicle law: (1) background on automated vehicles, (2) the regulation of automated vehicles, (3) civil liability for automated vehicle crashes, (4) data security and privacy, and (5) criminal law-- |
| data science in law: Responsible Data Science Peter C. Bruce, Grant Fleming, 2021-04-13 Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians. |
| data science in law: Argumentation Methods for Artificial Intelligence in Law Douglas Walton, 2005-09-30 Use of argumentation methods applied to legal reasoning is a relatively new field of study. The book provides a survey of the leading problems, and outlines how future research using argumentation-based methods show great promise of leading to useful solutions. The problems studied include not only these of argument evaluation and argument invention, but also analysis of specific kinds of evidence commonly used in law, like witness testimony, circumstantial evidence, forensic evidence and character evidence. New tools for analyzing these kinds of evidence are introduced. |
| data science in law: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| data science in law: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science in law: Research Handbook on Big Data Law Roland Vogl, 2021-05-28 This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains. |
| data science in law: Personal Data Protection and Legal Developments in the European Union Maria Tzanou, 2020-05-08 This book analyzes the latest advancements and developments in personal data protection in the European Union-- |
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Sarah J. Aristil, Noah E. Duncan, and Melissa J. Hopkins Data …
problem and approach, Part III details the methods of data collection, Part IV provides analysis of the data, and Part V suggests possible local and federal utilization plans based upon findings, …
Artificial Intelligence & Machine Learning: Emerging Legal and …
It begins with a brief discussion of how antitrust law and economics approach questions surrounding Big Data and AI/ML. The report then surveys litigation and enforcement actions …
The ‘rule of law implications of data-driven decision-
In this paper we discuss three major challenges data-driven ADM poses to the Rule Law: law as a normative enterprise, law as a causative enterprise and law as a moral enterprise. 1. Introduction.
Digital Commons @ American University Washington College …
Nov 16, 2023 · This paper sets out the issues of copyright ownership and risk of copyright infringement liability raised by data science research use of data held by public bodies (in …
DATA SCIENCE, DATA CRIME AND THE LAW - SSRN
data science and law could mutually help each other by identifying the ethical and legal devices necessary to enable big data analytic techniques to identify the key stages at which data …
Open Science and Data Protection: Engaging Scientific and …
The legal debate on the relationship between open science and data protection requires the involvement of several fields of knowledge: legal, from diferent branches of law, ethical and …
Using Data Governance and Data Management in Law …
Data governance concerns the control (planning and management) of actions, including policies, organizational structures, and formal procedures. Data management concerns the execution …
Law NUJS Kolkata WB National University of Juridical Sciences …
LL.M in Data Science and Data Protection Law Admission/2024-2025/02 Eligibility: Dated: 24-05-2024 Bachelor's degree in Law or an equivalent examination from an accredited University …
Data Science Strategy - Centers for Disease Control and …
A growing body of research now indicates that application of novel data and data science tools, methods, and techniques can help address critical public health needs, including injury and …
Best Data Practices in AI Conference - professional.brown.edu
Data Science Initiative and the School of Professional Studies on a conference that brought together business, technical, academic and legal experts in data science, law, and ethics to …
Law And Data Science - finder-lbs.com
in Data Science and Law Vanessa Mak,Eric Tjong Tjin Tai,Anna Berlee,2024-08-06 This thoroughly updated Research Handbook examines the recent exponential growth of data use …
THE LAW AND BIG DATA - Cornell University
In this Article we critically examine the use of Big Data in the legal system. Big Data is driving a trend towards behavioral optimization and “personalized law,” in which legal decisions and …
MODERN LAW REVIEW - New York University School of Law
In this article I will test the interface between law and data-driven agency by understanding law in terms of information, assuming that we cannot take for granted that law will interact with an …
The law in computation: What machine learning, artificial …
Nov 13, 2023 · We present cases for which examining law in computation illuminates how new technological processes potentially mitigate, exacerbate, or mask human biases present in …
1. Introduction to the Research Handbook in Data Science and …
For law and legal research the rise of data science is important for two reasons. First of all, data science gives rise to many legal issues which have not yet been fully investigated.
Fairness in Algorithmic Decisions - GitHub Pages
collaboration of data science, law and public policy And to fairness models fitting legislation and jurisprudence • Recent major works : Abu Elyounes ; Xiang ; Wachter & al. ; Hacker ; Kirat,
Bachelor of Science in Data Sciences for Justice, Law, and …
growth of large scale data sources in today’s technology-driven world, data science can provide unique insights on the nature of crime, terrorism, law, and the workings of the justice system. …
MICROSOFT DATA SCIENCE & LAW FORUM - Die Europäische …
Apr 24, 2018 · Microsoft’s ‘Data Science & Law Forum’ explored the various dimensions of AI; the changes AI will bring to the legal system; how competition, intellectual property and product …
Law And Data Science - nodejstest.schellbrothers.com
analysts to map the emerging field of data science and law for the first time uncovering the challenges opportunities and fault lines that arise as these groups are increasingly thrown …
Data, Prediction, and Law - University of California, Berkeley
Data, Prediction, and Law allows students to explore different data sources that scholars and government officials use to make generalizations and predictions in the realm of law. The …
Sarah J. Aristil, Noah E. Duncan, and Melissa J. Hopkins Data …
problem and approach, Part III details the methods of data collection, Part IV provides analysis of the data, and Part V suggests possible local and federal utilization plans based upon findings, …
Artificial Intelligence & Machine Learning: Emerging Legal and …
It begins with a brief discussion of how antitrust law and economics approach questions surrounding Big Data and AI/ML. The report then surveys litigation and enforcement actions …
The ‘rule of law implications of data-driven decision-
In this paper we discuss three major challenges data-driven ADM poses to the Rule Law: law as a normative enterprise, law as a causative enterprise and law as a moral enterprise. 1. Introduction.
Digital Commons @ American University Washington College …
Nov 16, 2023 · This paper sets out the issues of copyright ownership and risk of copyright infringement liability raised by data science research use of data held by public bodies (in …
DATA SCIENCE, DATA CRIME AND THE LAW - SSRN
data science and law could mutually help each other by identifying the ethical and legal devices necessary to enable big data analytic techniques to identify the key stages at which data …
Open Science and Data Protection: Engaging Scientific and …
The legal debate on the relationship between open science and data protection requires the involvement of several fields of knowledge: legal, from diferent branches of law, ethical and …
Using Data Governance and Data Management in Law …
Data governance concerns the control (planning and management) of actions, including policies, organizational structures, and formal procedures. Data management concerns the execution of …
Law NUJS Kolkata WB National University of Juridical …
LL.M in Data Science and Data Protection Law Admission/2024-2025/02 Eligibility: Dated: 24-05-2024 Bachelor's degree in Law or an equivalent examination from an accredited University with …
Data Science Strategy - Centers for Disease Control and …
A growing body of research now indicates that application of novel data and data science tools, methods, and techniques can help address critical public health needs, including injury and …
Best Data Practices in AI Conference - professional.brown.edu
Data Science Initiative and the School of Professional Studies on a conference that brought together business, technical, academic and legal experts in data science, law, and ethics to …
Law And Data Science - finder-lbs.com
in Data Science and Law Vanessa Mak,Eric Tjong Tjin Tai,Anna Berlee,2024-08-06 This thoroughly updated Research Handbook examines the recent exponential growth of data use in society …
