Download or read book Data Science in the Public Interest written by Joshua D. Hawley and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making"--

Download or read book Data Science in the Public Interest Improving Government Performance in the Workforce written by Joshua D. Hawley and published by W.E. Upjohn Institute. This book was released on 2020-07-22 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making.

Download or read book Public Policy Analytics written by Ken Steif and published by CRC Press. This book was released on 2021-08-18 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Ethical Data Science written by Anne L. Washington and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Ethical Data Science written by Anne L. Washington and published by Oxford University Press. This book was released on 2023 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Can data science truly serve the public interest? Data-driven analysis shapes many interpersonal, consumer, and cultural experiences yet scientific solutions to social problems routinely stumble. All too often, predictions remain solely a technocratic instrument that sets financial interests against service to humanity. Amidst a growing movement to use science for positive change, Anne L. Washington offers a solution-oriented approach to the ethical challenges of data science. Ethical Data Science empowers those striving to create predictive data technologies that benefit more people. As one of the first books on public interest technology, it provides a starting point for anyone who wants human values to counterbalance the institutional incentives that drive computational prediction. It argues that data science prediction embeds administrative preferences that often ignore the disenfranchised. The book introduces the prediction supply chain to highlight moral questions alongside the interlocking legal and commercial interests influencing data science. Structured around a typical data science workflow, the book systematically outlines the potential for more nuanced approaches to transforming data into meaningful patterns. Drawing on arts and humanities methods, it encourages readers to think critically about the full human potential of data science step-by-step. Situating data science within multiple layers of effort exposes dependencies while also pinpointing opportunities for research ethics and policy interventions. This approachable process lays the foundation for broader conversations with a wide range of audiences. Practitioners, academics, students, policy makers, and legislators can all learn how to identify social dynamics in data trends, reflect on ethical questions, and deliberate over solutions. The book proves the limits of predictive technology controlled by the few and calls for more inclusive data science.

Download or read book Data Feminism written by Catherine D'Ignazio and published by MIT Press. This book was released on 2023-10-03 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

Download or read book Elgar Encyclopedia of Law and Data Science written by Comandé, Giovanni and published by Edward Elgar Publishing. This book was released on 2022-02-18 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Science for Social Good written by Massimo Lapucci and published by Springer Nature. This book was released on 2021-10-13 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of reflections by thought leaders at first-mover organizations in the exploding field of "Data Science for Social Good", meant as the application of knowledge from computer science, complex systems and computational social science to challenges such as humanitarian response, public health, sustainable development. The book provides both an overview of scientific approaches to social impact – identifying a social need, targeting an intervention, measuring impact – and the complementary perspective of funders and philanthropies that are pushing forward this new sector. This book will appeal to students and researchers in the rapidly growing field of data science for social impact, to data scientists at companies whose data could be used to generate more public value, and to decision makers at nonprofits, foundations, and agencies that are designing their own agenda around data.

Download or read book Geospatial Health Data written by Paula Moraga and published by CRC Press. This book was released on 2019-11-26 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Download or read book Privacy Big Data and the Public Good written by Julia Lane and published by Cambridge University Press. This book was released on 2014-06-09 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive amounts of data on human beings can now be analyzed. Pragmatic purposes abound, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet 'big data' can also be harnessed to serve the public good: scientists can use big data to do research that improves the lives of human beings, improves government services, and reduces taxpayer costs. In order to achieve this goal, researchers must have access to this data - raising important privacy questions. What are the ethical and legal requirements? What are the rules of engagement? What are the best ways to provide access while also protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss? The goal of this book is to answer some of these questions. The book's authors paint an intellectual landscape that includes legal, economic, and statistical frameworks. The authors also identify new practical approaches that simultaneously maximize the utility of data access while minimizing information risk.

Download or read book Data Science for Public Policy written by Jeffrey C. Chen and published by Springer Nature. This book was released on 2021-09-01 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

Download or read book How to Think about Data Science written by Diego Miranda-Saavedra and published by CRC Press. This book was released on 2022-12-23 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a timely and critical introduction for those interested in what data science is (and isn’t), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist’s approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist’s approach to explaining data science through questions and examples.

Download or read book Power to the Public written by Tara Dawson McGuinness and published by Princeton University Press. This book was released on 2023-04-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Worth a read for anyone who cares about making change happen.”—Barack Obama A powerful new blueprint for how governments and nonprofits can harness the power of digital technology to help solve the most serious problems of the twenty-first century As the speed and complexity of the world increases, governments and nonprofit organizations need new ways to effectively tackle the critical challenges of our time—from pandemics and global warming to social media warfare. In Power to the Public, Tara Dawson McGuinness and Hana Schank describe a revolutionary new approach—public interest technology—that has the potential to transform the way governments and nonprofits around the world solve problems. Through inspiring stories about successful projects ranging from a texting service for teenagers in crisis to a streamlined foster care system, the authors show how public interest technology can make the delivery of services to the public more effective and efficient. At its heart, public interest technology means putting users at the center of the policymaking process, using data and metrics in a smart way, and running small experiments and pilot programs before scaling up. And while this approach may well involve the innovative use of digital technology, technology alone is no panacea—and some of the best solutions may even be decidedly low-tech. Clear-eyed yet profoundly optimistic, Power to the Public presents a powerful blueprint for how government and nonprofits can help solve society’s most serious problems.

Download or read book The Application of Emerging Technology and Blockchain in the Insurance Industry written by Kiran Sood and published by CRC Press. This book was released on 2024-02-20 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a unique guide to the disruptions, innovations, and opportunities that technology provides the insurance sector and acts as an academic/industry-specific guide for creating operational effectiveness, managing risk, improving financials, and retaining customers. It also contains the current philosophy and actionable strategies from a wide range of contributors who are experts on the topic. It logically explains why traditional ways of doing business will soon become irrelevant and therefore provides an alternative choice by embracing technology. Practitioners and students alike will find value in the support for understanding practical implications of how technology has brought innovation and modern methods to measure, control, and evaluation price risk in the insurance business. It will help insurers reduce operational costs, strengthen customer interactions, target potential customers to provide usage-based insurance, and optimize the overall business. Retailers and industry giants have made significant strides in adopting digital platforms to deliver a satisfying customer experience. Insurance companies must adjust their business models and strategies to remain competitive and take advantage of technology. Insurance companies are increasingly investing in IT and related technologies to improve customer experience and reduce operational costs. Innovation through new technologies is a key driver of change in the financial sector which is often accompanied by uncertainty and doubt. This book will play a pivotal role in risk management through fraud detection, regulatory compliances, and claim settlement leading to overall satisfaction of customers.

Download or read book Encyclopedia of Data Science and Machine Learning written by Wang, John and published by IGI Global. This book was released on 2023-01-20 with total page 3296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Download or read book Federal Data Science written by Feras A. Batarseh and published by Academic Press. This book was released on 2017-09-21 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making. No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective. Offers a range of data science models, engineering tools, and federal use-cases Provides foundational observations into government data resources and requirements Introduces experiences and examples of data openness from the US and other countries A step-by-step guide for the conversion of government towards data-driven policy making Focuses on presenting data models that work within the constraints of the US government Presents the why, the what, and the how of injecting AI into federal culture and software systems

Download or read book Statistics in the Public Interest written by Alicia L. Carriquiry and published by Springer Nature. This book was released on 2022-04-22 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume surveys a variety of topics in statistics and the social sciences in memory of the late Stephen Fienberg. The book collects submissions from a wide range of contemporary authors to explore the fields in which Fienberg made significant contributions, including contingency tables and log-linear models, privacy and confidentiality, forensics and the law, the decennial census and other surveys, the National Academies, Bayesian theory and methods, causal inference and causes of effects, mixed membership models, and computing and machine learning. Each section begins with an overview of Fienberg’s contributions and continues with chapters by Fienberg’s students, colleagues, and collaborators exploring recent advances and the current state of research on the topic. In addition, this volume includes a biographical introduction as well as a memorial concluding chapter comprised of entries from Stephen and Joyce Fienberg’s close friends, former students, colleagues, and other loved ones, as well as a photographic tribute.