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| ethics and data science: Ethics and Data Science Mike Loukides, Hilary Mason, DJ Patil, 2018-07-25 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. |
| ethics and data science: Data Science Ethics David Martens, 2022-03-24 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. |
| ethics and data science: 97 Things About Ethics Everyone in Data Science Should Know Bill Franks, 2020-08-06 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 Heartâ??Hugh Watson Understanding Passive Versus Proactive Ethicsâ??Bill Schmarzo |
| ethics and data science: Ethics of Data and Analytics Kirsten Martin, 2022-05-12 The ethics of data and analytics, in many ways, is no different than any endeavor to find the right answer. When a business chooses a supplier, funds a new product, or hires an employee, managers are making decisions with moral implications. The decisions in business, like all decisions, have a moral component in that people can benefit or be harmed, rules are followed or broken, people are treated fairly or not, and rights are enabled or diminished. However, data analytics introduces wrinkles or moral hurdles in how to think about ethics. Questions of accountability, privacy, surveillance, bias, and power stretch standard tools to examine whether a decision is good, ethical, or just. Dealing with these questions requires different frameworks to understand what is wrong and what could be better. Ethics of Data and Analytics: Concepts and Cases does not search for a new, different answer or to ban all technology in favor of human decision-making. The text takes a more skeptical, ironic approach to current answers and concepts while identifying and having solidarity with others. Applying this to the endeavor to understand the ethics of data and analytics, the text emphasizes finding multiple ethical approaches as ways to engage with current problems to find better solutions rather than prioritizing one set of concepts or theories. The book works through cases to understand those marginalized by data analytics programs as well as those empowered by them. Three themes run throughout the book. First, data analytics programs are value-laden in that technologies create moral consequences, reinforce or undercut ethical principles, and enable or diminish rights and dignity. This places an additional focus on the role of developers in their incorporation of values in the design of data analytics programs. Second, design is critical. In the majority of the cases examined, the purpose is to improve the design and development of data analytics programs. Third, data analytics, artificial intelligence, and machine learning are about power. The discussion of power—who has it, who gets to keep it, and who is marginalized—weaves throughout the chapters, theories, and cases. In discussing ethical frameworks, the text focuses on critical theories that question power structures and default assumptions and seek to emancipate the marginalized. |
| ethics and data science: Ethics of Big Data Kord Davis, 2012-09-13 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 |
| ethics and data science: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 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. |
| ethics and data science: Leveraging Data Science for Global Health Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai, 2020-07-31 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. |
| ethics and data science: 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. |
| ethics and data science: 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. |
| ethics and data science: 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. |
| ethics and data science: The Ethics of Biomedical Big Data Brent Daniel Mittelstadt, Luciano Floridi, 2016-08-03 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. |
| ethics and data science: Ethical Data Mining Applications for Socio-Economic Development Hakikur Rahman, Isabel Ramos, 2013-05-31 This book provides an overview of data mining techniques under an ethical lens, investigating developments in research best practices and examining experimental cases to identify potential ethical dilemmas in the information and communications technology sector--Provided by publisher. |
| ethics and data science: 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. |
| ethics and data science: AI Ethics Mark Coeckelbergh, 2020-04-07 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. |
| ethics and data science: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 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. |
| ethics and data science: Ethical Data and Information Management Katherine O'Keefe, Daragh O Brien, 2018-05-03 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. |
| ethics and data science: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 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. |
| ethics and data science: Research Ethics for Scientists C. Neal Stewart, Jr., 2011-09-19 Research Ethics for Scientists is about best practices in all the major areas of research management and practice that are common to scientific researchers, especially those in academia. Aimed towards the younger scientist, the book critically examines the key areas that continue to plague even experienced and well-meaning science professionals. For ease of use, the book is arranged in functional themes and units that every scientist recognizes as crucial for sustained success in science; ideas, people, data, publications and funding. These key themes will help to highlight the elements of successful and ethical research as well as challenging the reader to develop their own ideas of how to conduct themselves within their work. Tackles the ethical issues of being a scientist rather than the ethical questions raised by science itself Case studies used for a practical approach Written by an experienced researcher and PhD mentor Accessible, user-friendly advice Indispensible companion for students and young scientists |
| ethics and data science: Ethics and Science Adam Briggle, Carl Mitcham, 2012-10-25 This book explores ethical issues at the interfaces of science, policy, religion and technology, cultivating the skills for critical analysis. |
| ethics and data science: The Ethical Algorithm Michael Kearns, Aaron Roth, 2020 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. |
| ethics and data science: Data Matters National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Government-University-Industry Research Roundtable, Planning Committee for the Workshop on Ethics, Data, and International Research Collaboration in a Changing World, 2019-01-28 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. |
| ethics and data science: Data Ethics Gry Hasselbalch, 2016 |
| ethics and data science: 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. |
| ethics and data science: Ethics for Bioengineering Scientists Howard Winet, 2021-12-08 This book introduces bioengineers and students who must generate and/or report scientific data to the ethical challenges they will face in preserving the integrity of their data. It provides the perspective of reaching ethical decisions via pathways that treat data as clients, to whom bioengineering scientists owe a responsibility that is an existential component of their professional identity. The initial chapters lay a historical, biological and philosophical foundation for ethics as a human activity, and data as a foundation of science. The middle chapters explore ethical challenges in lay, engineering, medical and bioengineering scientist settings. These chapters focus on micro-ethics, individual behavior, and cases that showcase the consequences of violating data integrity. Macro-ethics, policy, is dealt with in the Enrichment sections at the end of the chapters, with essay problems and subjects for debates (in a classroom setting). The book can be used for individual study, using links in the Enrichment sections to access cases and media presentations, like PBS’ Ethics in America. The final chapters explore the impact of bioengineering science ethics on patients, via medical product development, its regulation by the FDA, and the contribution of data integrity violation to product failure. The book was developed for advanced undergraduate and graduate students in bioengineering. It also contains much needed material that researchers and academics would find valuable (e.g., FDA survey, and lab animal research justification). Introduces an approach to ethical decision making based on treating data as clients Compares the ethics of three professions; engineering, medicine and bioengineering Provides five moral theories to choose from for evaluating ethical decisions, and includes a procedure for applying them to moral analysis, and application of the procedure to example cases. Examines core concepts, like autonomy, confidentiality, conflict of interest and justice Explains the process of developing a medical product under FDA regulation Explores the role of lawyers and the judiciary in product development, including intellectual property protection Examines a range of ethical cases, from the historical Tuskegee autonomy case to the modern CRISPR-Cas9 patent case. Howard Winet, PhD is an Adjunct Professor recall, Orthopaedic Surgery and Bioengineering at University of California, Los Angeles. |
| ethics and data science: Ethical Practice of Statistics and Data Science Rochelle Tractenberg, 2023-11-25 Ethical Practice of Statistics and Data Science is intended to prepare people to fully assume their responsibilities to practice statistics and data science ethically. Aimed at early career professionals, practitioners, and mentors or supervisors of practitioners, the book supports the ethical practice of statistics and data science, with an emphasis on how to earn the designation of, and recognize, “the ethical practitioner”. The book features 47 case studies, each mapped to the Data Science Ethics Checklist (DSEC); Data Ethics Framework (DEFW); the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice; and the Association of Computing Machinery (ACM) Code of Ethics. It is necessary reading for students enrolled in any data intensive program, including undergraduate or graduate degrees in (bio-)statistics, business/analytics, or data science. Managers, leaders, supervisors, and mentors who lead data-intensive teams in government, industry, or academia would also benefit greatly from this book. This is a companion volume to Ethical Reasoning For A Data-Centered World, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the ASA and ACM Ethical Guidelines/Code of Ethics. |
| ethics and data science: The Big Data Agenda Annika Richterich, 2018-04-13 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. |
| ethics and data science: Scientific Integrity and Ethics in the Geosciences Linda C. Gundersen, 2017-11-20 Science is built on trust. The assumption is that scientists will conduct their work with integrity, honesty, and a strict adherence to scientific protocols. Written by geoscientists for geoscientists, Scientific Integrity and Ethics in the Geosciences acquaints readers with the fundamental principles of scientific ethics and shows how they apply to everyday work in the classroom, laboratory, and field. Resources are provided throughout to help discuss and implement principles of scientific integrity and ethics. Volume highlights include: Examples of international and national codes and policies Exploration of the role of professional societies in scientific integrity and ethics References to scientific integrity and ethics in publications and research data Discussion of science integrity, ethics, and geoethics in education Extensive coverage of data applications Scientific Integrity and Ethics in the Geosciences is a valuable resource for students, faculty, instructors, and scientists in the geosciences and beyond. It is also useful for geoscientists working in industry, government, and policymaking. Read an interview with the editors to find out more: https://eos.org/editors-vox/ethics-crucial-for-the-future-of-the-geosciences |
| ethics and data science: Research Ethics for Social Scientists Mark Israel, Iain Hay, 2006-06-29 Introduces students to ethical theory and philosophy. This work provides practical guidance on what ethical theory means for research practice; and, offers case studies to give real examples of ethics in research action. |
| ethics and data science: Research Ethics for Students in the Social Sciences Jaap Bos, 2020-10-16 This open access textbook offers a practical guide into research ethics for undergraduate students in the social sciences. A step-by-step approach of the most viable issues, in-depth discussions of case histories and a variety of didactical tools will aid the student to grasp the issues at hand and help him or her develop strategies to deal with them. This book addresses problems and questions that any bachelor student in the social sciences should be aware of, including plagiarism, data fabrication and other types of fraud, data augmentation, various forms of research bias, but also peer pressure, issues with confidentiality and questions regarding conflicts of interest. Cheating, ‘free riding’, and broader issues that relate to the place of the social sciences in society are also included. The book concludes with a step-by-step approach designed to coach a student through a research application process. |
| ethics and data science: What Is Data Science? Mike Loukides, 2011-04-10 We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that data is the next Intel Inside. But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of data-driven apps. Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by data science. A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products. |
| ethics and data science: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| ethics and data science: Ethics and the Practice of Forensic Science Robin T. Bowen, 2017-09-20 While one would hope that forensic scientists, investigators, and experts are intrinsically ethical by nature, the reality is that these individuals have morality as varied as the general population. These professionals confront ethical dilemmas every day, some with clear-cut protocols and others that frequently have no definitive answers. Since the publication of the first edition of Ethics and the Practice of Forensic Science, the field of forensic science has continued to see its share of controversy. This runs the gamut of news stories from investigators, lab personnel, or even lab directors falsifying results, committing perjury, admitting to fraud, to overturned convictions, questions about bias, ethics, and what constitutes an expert on the witness stand. This fully updated edition tackles all these issues—including some specific instances and cases of unethical behavior—and addresses such salient issues as accreditation requirements, standardization of ethical codes, examiner certification, and standards for education and training. The new edition provides: A new chapter on the Ferguson Effect faced by the criminal justice system The context of forensic science ethics in relation to general scientific ethics, measurement uncertainty, and ethics in criminal justice Ethical conundrums and real-world examples that forensic scientists confront every day The ethics and conduct codes of 20 different forensic and scientific professional organizations An outline of the National Academies of Science (NAS) recommendations and progress made on ethics in forensic science since the release of the NAS report Ethics and the Practice of Forensic Science, Second Edition explores the range of ethical issues facing those who work in the forensic sciences—highlights the complicated nature of ethics and decision-making at the crime scene, in the lab, and in the courts. The book serves both as an essential resource for laboratories to train their employees and as an invaluable textbook for the growing number of courses on ethics in criminal justice and forensic science curricula. Accompanying PowerPoint® slides and an Instructor’s Manual with Test Bank are available to professors upon qualifying course adoption. |
| ethics and data science: Research Ethics Gary Comstock, 2013-01-03 Education in the responsible conduct of research typically takes the form of online instructions about rules, regulations, and policies. Research Ethics takes a novel approach and emphasizes the art of philosophical decision-making. Part A introduces egoism and explains that it is in the individual's own interest to avoid misconduct, fabrication of data, plagiarism and bias. Part B explains contractualism and covers issues of authorship, peer review and responsible use of statistics. Part C introduces moral rights as the basis of informed consent, the use of humans in research, mentoring, intellectual property and conflicts of interests. Part D uses two-level utilitarianism to explore the possibilities and limits of the experimental use of animals, duties to the environment and future generations, and the social responsibilities of researchers. This book brings a fresh perspective to research ethics and will engage the moral imaginations of graduate students in all disciplines. |
| ethics and data science: Data Driven DJ Patil, Hilary Mason, 2015-01-05 Succeeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt. You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century. You’ll explore: Data scientist skills—and why every company needs a Spock How the benefits of giving company-wide access to data outweigh the costs Why data-driven organizations use the scientific method to explore and solve data problems Key questions to help you develop a research-specific process for tackling important issues What to consider when assembling your data team Developing processes to keep your data team (and company) engaged Choosing technologies that are powerful, support teamwork, and easy to use and learn |
| ethics and data science: Data and Society Anne Beaulieu, Sabina Leonelli, 2021-10-27 Data and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the Big Data hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work. |
| ethics and data science: The Data of Ethics Herbert Spencer, 1901 |
| ethics and data science: Ethics in Computing Joseph Migga Kizza, 2016-05-09 This textbook raises thought-provoking questions regarding our rapidly-evolving computing technologies, highlighting the need for a strong ethical framework in our computer science education. Ethics in Computing offers a concise introduction to this topic, distilled from the more expansive Ethical and Social Issues in the Information Age. Features: introduces the philosophical framework for analyzing computer ethics; describes the impact of computer technology on issues of security, privacy and anonymity; examines intellectual property rights in the context of computing; discusses such issues as the digital divide, employee monitoring in the workplace, and health risks; reviews the history of computer crimes and the threat of cyberbullying; provides coverage of the ethics of AI, virtualization technologies, virtual reality, and the Internet; considers the social, moral and ethical challenges arising from social networks and mobile communication technologies; includes discussion questions and exercises. |
| ethics and data science: Learning Apache Drill Charles Givre, Paul Rogers, 2018-11-02 Get up to speed with Apache Drill, an extensible distributed SQL query engine that reads massive datasets in many popular file formats such as Parquet, JSON, and CSV. Drill reads data in HDFS or in cloud-native storage such as S3 and works with Hive metastores along with distributed databases such as HBase, MongoDB, and relational databases. Drill works everywhere: on your laptop or in your largest cluster. In this practical book, Drill committers Charles Givre and Paul Rogers show analysts and data scientists how to query and analyze raw data using this powerful tool. Data scientists today spend about 80% of their time just gathering and cleaning data. With this book, you’ll learn how Drill helps you analyze data more effectively to drive down time to insight. Use Drill to clean, prepare, and summarize delimited data for further analysis Query file types including logfiles, Parquet, JSON, and other complex formats Query Hadoop, relational databases, MongoDB, and Kafka with standard SQL Connect to Drill programmatically using a variety of languages Use Drill even with challenging or ambiguous file formats Perform sophisticated analysis by extending Drill’s functionality with user-defined functions Facilitate data analysis for network security, image metadata, and machine learning |
| ethics and data science: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| ethics and data science: Ethics in Science John D'Angelo, 2012-03-27 Providing the tools necessary for robust debate, Ethics in Science: Ethical Misconduct in Scientific Research explains various forms of scientific misconduct and describes ethical controversies that have occurred in research. The first part of the book includes a description of a variety of ethical violations, why they occur, how they are handled, and what can be done to prevent them along with a discussion of the peer-review process. The second part of the book presents real-life case studies that review the known facts, allowing readers to decide for themselves whether an ethical violation has occurred and if so, what should be done. Discussing the difference between bad science and bad ethics and how to prevent scientific misconduct, this book explains the various forms of scientific misconduct and provides resources for guided discussion of topical controversies. |
Noam Chomsky: The False Promise of ChatGPT - MacRumors …
and debase our ethics by incorporating into our technology a fundamentally flawed conception of language and knowledge. OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney are …
Apple Quietly Fixed Zero-Day Exploit Used in Paragon Spyware …
5 days ago · Apple today quietly updated the list of security fixes that were introduced in iOS 18.3.1, noting a previously undisclosed fix for a zero-day vulnerability affecting the Messages …
Trump Tells Tim Cook to Stop Building iPhones in India
Apr 12, 2001 · President Donald Trump has asked Apple CEO Tim Cook to halt the company's manufacturing expansion in India, in a potential disruption of Apple's plan to shift iPhone …
What audio interfaces are you using? | MacRumors Forums
Jun 15, 2009 · Totally agree. It is sad that people are so willing to abandon ethics and morals for a cheap mediocre product through pure rationalization. What you say isn't new - a quick search …
Federal Court Blocks Trump Tariffs That Could Have
May 29, 2025 · Like it or not, those fixes are only supported on the left. They require a a commitment to democracy. End gerrymandering. Overturn Citizens United. Get rid of the …
Siri Rumored to Take a Backseat at WWDC 2025 - MacRumors …
Apr 12, 2001 · Apple is likely to keep discussion of Siri to a minimum at WWDC 2025 as it focuses on other Apple Intelligence enhancements, according to Bloomberg's Mark Gurman and Drake …
Apple Again Named the World's Most Valuable Brand
Apr 12, 2001 · Apple has been named the most valuable global brand for the fourth consecutive year, according to the 2025 edition of Kantar's BrandZ report, with its brand now valued at …
Apple Says Personalized Siri Features Shown at WWDC Last Year …
Jul 16, 2013 · Exactly this was to not say anything that will make the lawsuit worst. But if they got proof this was no working the lawsuit will succeed. But company ethics for any company not …
Testing Samsung's Super Thin Galaxy S25 Edge - MacRumors …
Jun 8, 2017 · The more I think about it, it's starting to seem more silly to me to have a thick and heavy phone in my pocket when I rarely see the wrong side of 40% charge on my 15 Pro on …
iPhone iPhone SimFree FREE tool (iUnlock) released.
Jul 1, 2007 · Except that it was blatantly illegal and what most with ethics would call "stealing". Plus, this isn't very cat and mouse, Apple doesn't give a crap, AT&T is just a means to an end …
Noam Chomsky: The False Promise of ChatGPT - MacRumors …
and debase our ethics by incorporating into our technology a fundamentally flawed conception of language and knowledge. OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney are …
Apple Quietly Fixed Zero-Day Exploit Used in Paragon Spyware …
5 days ago · Apple today quietly updated the list of security fixes that were introduced in iOS 18.3.1, noting a previously undisclosed fix for a zero-day vulnerability affecting the Messages …
Trump Tells Tim Cook to Stop Building iPhones in India
Apr 12, 2001 · President Donald Trump has asked Apple CEO Tim Cook to halt the company's manufacturing expansion in India, in a potential disruption of Apple's plan to shift iPhone …
What audio interfaces are you using? | MacRumors Forums
Jun 15, 2009 · Totally agree. It is sad that people are so willing to abandon ethics and morals for a cheap mediocre product through pure rationalization. What you say isn't new - a quick search …
Federal Court Blocks Trump Tariffs That Could Have
May 29, 2025 · Like it or not, those fixes are only supported on the left. They require a a commitment to democracy. End gerrymandering. Overturn Citizens United. Get rid of the …
Siri Rumored to Take a Backseat at WWDC 2025 - MacRumors …
Apr 12, 2001 · Apple is likely to keep discussion of Siri to a minimum at WWDC 2025 as it focuses on other Apple Intelligence enhancements, according to Bloomberg's Mark Gurman and Drake …
Apple Again Named the World's Most Valuable Brand
Apr 12, 2001 · Apple has been named the most valuable global brand for the fourth consecutive year, according to the 2025 edition of Kantar's BrandZ report, with its brand now valued at …
Apple Says Personalized Siri Features Shown at WWDC Last Year …
Jul 16, 2013 · Exactly this was to not say anything that will make the lawsuit worst. But if they got proof this was no working the lawsuit will succeed. But company ethics for any company not …
Testing Samsung's Super Thin Galaxy S25 Edge - MacRumors …
Jun 8, 2017 · The more I think about it, it's starting to seem more silly to me to have a thick and heavy phone in my pocket when I rarely see the wrong side of 40% charge on my 15 Pro on …
iPhone iPhone SimFree FREE tool (iUnlock) released.
Jul 1, 2007 · Except that it was blatantly illegal and what most with ethics would call "stealing". Plus, this isn't very cat and mouse, Apple doesn't give a crap, AT&T is just a means to an end …
