Download or read book Ethics and Data Science written by Mike Loukides and published by "O'Reilly Media, Inc.". This book was released on 2018-07-25 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Download or read book Data Science Ethics written by David Martens and published by Oxford University Press. This book was released on 2022-03-24 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data.

Download or read book 97 Things About Ethics Everyone in Data Science Should Know written by Bill Franks and published by O'Reilly Media. This book was released on 2020-08-06 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most of the high-profile cases of real or perceived unethical activity in data science arenâ??t matters of bad intent. Rather, they occur because the ethics simply arenâ??t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Conceptâ??Tim Wilson How to Approach Ethical Transparencyâ??Rado Kotorov Unbiased ≠ Fairâ??Doug Hague Rules and Rationalityâ??Christof Wolf Brenner The Truth About AI Biasâ??Cassie Kozyrkov Cautionary Ethics Talesâ??Sherrill Hayes Fairness in the Age of Algorithmsâ??Anna Jacobson The Ethical Data Storytellerâ??Brent Dykes Introducing Ethicizeâ?¢, the Fully AI-Driven Cloud-Based Ethics Solution!â??Brian Oâ??Neill Be Careful with "Decisions of the Heart"â??Hugh Watson Understanding Passive Versus Proactive Ethicsâ??Bill Schmarzo

Download or read book Responsible Data Science written by Peter C. Bruce and published by John Wiley & Sons. This book was released on 2021-04-13 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Leveraging Data Science for Global Health written by Leo Anthony Celi and published by Springer Nature. This book was released on 2020-07-31 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Download or read book Data Science for Undergraduates written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-11-11 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The Ethics of Biomedical Big Data written by Brent Daniel Mittelstadt and published by Springer. This book was released on 2016-08-03 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward.

Download or read book Modern Data Science with R written by Benjamin S. Baumer and published by CRC Press. This book was released on 2021-03-31 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book The Ethical Algorithm written by Michael Kearns and published by . This book was released on 2020 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms have made our lives more efficient and entertaining--but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design.

Download or read book Data Science written by John D. Kelleher and published by MIT Press. This book was released on 2018-04-13 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Feminism written by Catherine D'Ignazio and published by MIT Press. This book was released on 2020-03-31 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 Human Centered Data Science written by Cecilia Aragon and published by MIT Press. This book was released on 2022-03-01 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.

Download or read book Ethics of Big Data written by Kord Davis and published by "O'Reilly Media, Inc.". This book was released on 2012-09-13 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are your organization’s policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. Authors Kord Davis and Doug Patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices. Both individuals and organizations have legitimate interests in understanding how data is handled. Your use of data can directly affect brand quality and revenue—as Target, Apple, Netflix, and dozens of other companies have discovered. With this book, you’ll learn how to align your actions with explicit company values and preserve the trust of customers, partners, and stakeholders. Review your data-handling practices and examine whether they reflect core organizational values Express coherent and consistent positions on your organization’s use of big data Define tactical plans to close gaps between values and practices—and discover how to maintain alignment as conditions change over time Maintain a balance between the benefits of innovation and the risks of unintended consequences

Download or read book Ethical Data and Information Management written by Katherine O'Keefe and published by Kogan Page Publishers. This book was released on 2018-05-03 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information and how we manage, process and govern it is becoming increasingly important as organizations ride the wave of the big data revolution. Ethical Data and Information Management offers a practical guide for people in organizations who are tasked with implementing information management projects. It sets out, in a clear and structured way, the fundamentals of ethics, and provides practical and pragmatic methods for organizations to embed ethical principles and practices into their management and governance of information. Written by global experts in the field, Ethical Data and Information Management is an important book addressing a topic high on the information management agenda. Key coverage includes how to build ethical checks and balances into data governance decision making; using quality management methods to assess and evaluate the ethical nature of processing during design; change methods to communicate ethical values; how to avoid common problems that affect ethical action; and how to make the business case for ethical behaviours.

Download or read book AI Ethics written by Mark Coeckelbergh and published by MIT Press. This book was released on 2020-04-07 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions—offering a compelling, necessary read for our ChatGPT era. Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions. Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein’s monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.

Download or read book Data Matters written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-01-28 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€"this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€"all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop.

Download or read book The Big Data Agenda written by Annika Richterich and published by University of Westminster Press. This book was released on 2018-04-13 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis. The book assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work. The Big Data Agenda concludes that the use of big data in research urgently needs to be considered from the vantage point of ethics and social justice. Drawing upon discourse ethics and critical data studies, Richterich argues that entanglements between big data research and technology/ internet corporations have emerged. In consequence, more opportunities for discussing and negotiating emerging research practices and their implications for societal values are needed.