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| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in data science: Ethics of Data and Analytics Kirsten Martin, 2022-05-12 Unique selling point: Applies business ethics to the use of analytics, data, and AI Core audience: Graduate and undergraduate business students Place in the market: Graduate and undergraduate textbook |
| ethical issues in 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 |
| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in data science: Ensuring Research Integrity and the Ethical Management of Data Sibinga, Cees Th. Smit, 2018-01-31 Data management technology is rapidly progressing, and with it comes the need for stricter rules that ensure the information being collected is handled appropriately. Ensuring Research Integrity and the Ethical Management of Data is an essential resource that examines the best approaches for providing quality research, as well as how to effectively manage that information in a reputable way. Featuring extensive research on relevant topics such as qualitative data collection, data sharing, data misinterpretation, and intellectual property, this scholarly publication is an ideal reference source for academicians, students, and researchers interested in current trends and techniques in ethical research and data management. |
| ethical issues in data science: Sharing Research Data to Improve Public Health in Africa National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on Population, 2015-09-18 Sharing research data on public health issues can promote expanded scientific inquiry and has the potential to advance improvements in public health. Although sharing data is the norm in some research fields, sharing of data in public health is not as firmly established. In March 2015, the National Research Council organized an international conference in Stellenbosch, South Africa, to explore the benefits of and barriers to sharing research data within the African context. The workshop brought together public health researchers and epidemiologists primarily from the African continent, along with selected international experts, to talk about the benefits and challenges of sharing data to improve public health, and to discuss potential actions to guide future work related to public health research data sharing. Sharing Research Data to Improve Public Health in Africa summarizes the presentations and discussions from this workshop. |
| ethical issues in 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. |
| ethical issues in data science: Big Data Analytics for Time-Critical Mobility Forecasting George A. Vouros, Gennady Andrienko, Christos Doulkeridis, Nikolaos Pelekis, Alexander Artikis, Anne-Laure Jousselme, Cyril Ray, Jose Manuel Cordero, David Scarlatti, 2020-06-23 This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities’ characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains. |
| ethical issues in 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 |
| ethical issues in 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. |
| ethical issues in data science: Ethical Issues in Covert, Security and Surveillance Research Ron Iphofen, Dónal O’Mathúna, 2021-12-09 The ebook edition of this title is Open Access and freely available to read online. Ethical Issues in Covert, Security and Surveillance Research showcases that it is only when the integrity of research is carefully pursued can users of the evidence produced be assured of its value and its ethical credentials. |
| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in data science: Multidisciplinary Approaches to Ethics in the Digital Era Taskiran, Meliha Nurdan, Pinarba?i, Fatih, 2021-03-18 The digital era has redefined our understanding of ethics as a multi-disciplinary phenomenon. The newness of the internet means it is still highly unregulated, which allows for rampant problems encountered by countless internet users. In order to establish a framework to protect digital citizenship, an academic understanding of online ethics is required. Multidisciplinary Approaches to Ethics in the Digital Era examines the concept of ethics in the digital environment through the framework of digitalization. Covering a broad range of topics including ethics in art, organizational ethics, and civil engineering ethics, this book is ideally designed for media professionals, sociologists, programmers, policymakers, government officials, academicians, researchers, and students. |
| ethical issues in 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. |
| ethical issues in 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. |
| ethical issues in data science: An Examination of Emerging Bioethical Issues in Biomedical Research National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, 2020-09-10 On February 26, 2020, the Board on Health Sciences Policy of the National Academies of Sciences, Engineering, and Medicine hosted a 1-day public workshop in Washington, DC, to examine current and emerging bioethical issues that might arise in the context of biomedical research and to consider research topics in bioethics that could benefit from further attention. The scope of bioethical issues in research is broad, but this workshop focused on issues related to the development and use of digital technologies, artificial intelligence, and machine learning in research and clinical practice; issues emerging as nontraditional approaches to health research become more widespread; the role of bioethics in addressing racial and structural inequalities in health; and enhancing the capacity and diversity of the bioethics workforce. This publication summarizes the presentations and discussions from the workshop. |
| ethical issues in data science: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| ethical issues in data science: Big Data Ethics in Research Nicolae Sfetcu, The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, GDPR). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. CONTENTS: Abstract 1. Introduction - 1.1 Definitions - 1.2 Big Data dimensions 2. Technology - 2.1 Applications - - 2.1.1 In research 3. Philosophical aspects 4. Legal aspects - 4.1 GDPR - - Stages of processing of personal data - - Principles of data processing - - Privacy policy and transparency - - Purposes of data processing - - Design and implicit confidentiality - - The (legal) paradox of Big Data 5. Ethical issues - Ethics in research - Awareness - Consent - Control - Transparency - Trust - Ownership - Surveillance and security - Digital identity - Tailored reality - De-identification - Digital inequality - Privacy 6. Big Data research Conclusions Bibliography DOI: 10.13140/RG.2.2.11054.46401 |
| ethical issues in data science: Data Science for Fake News Deepak P, Tanmoy Chakraborty, Cheng Long, Santhosh Kumar G, 2021-04-29 This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research. |
| ethical issues in data science: Artificial Intelligence for a Better Future Bernd Carsten Stahl, 2021-03-17 This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical problems? This book presents an innovative answer to the question by presenting a different perspective on AI and its ethical consequences. Instead of looking at individual AI techniques, applications or ethical issues, we can understand AI as a system of ecosystems, consisting of numerous interdependent technologies, applications and stakeholders. Developing this idea, the book explores how AI ecosystems can be shaped to foster human flourishing. Drawing on rich empirical insights and detailed conceptual analysis, it suggests practical measures to ensure that AI is used to make the world a better place. |
| ethical issues in data science: Radical Solutions and Open Science Daniel Burgos, 2020-05-14 This open access book presents how Open Science is a powerful tool to boost Higher Education. The book introduces the reader into Open Access, Open Technology, Open Data, Open Research results, Open Licensing, Open Accreditation, Open Certification, Open Policy and, of course, Open Educational Resources. It brings all these key topics from major players in the field; experts that present the current state of the art and the forthcoming steps towards a useful and effective implementation. This book presents radical, transgenic solutions for recurrent and long-standing problems in Higher Education. Every chapter presents a clear view and a related solution to make Higher Education progress and implement tools and strategies to improve the user’s performance and learning experience. This book is part of a trilogy with companion volumes on Radical Solutions & Learning Analytics and Radical Solutions & eLearning. |
| ethical issues in data science: Scholarly Ethics and Publishing: Breakthroughs in Research and Practice Management Association, Information Resources, 2019-03-01 A vital component of any publishing project is the ethical dimensions, which can refer to varied categories of practice: from conducting a proper peer review to using proper citation in research. With the implementation of technology in research and publishing, it is important for today’s researchers to address the standards of scientific research and publishing practices to avoid unethical behavior. Scholarly Ethics and Publishing: Breakthroughs in Research and Practice is an essential reference source that discusses various aspects of ethical values in academic settings including methods and tools to prevent and detect plagiarism, strategies for the principled gathering of data, and best practices for conducting and citing research. It also assists researchers in navigating the field of scholarly publishing through a careful analysis of multidisciplinary research topics and recent trends in the industry. Highlighting a range of pertinent topics such as academic writing, publication process, and research methodologies, this publication is an ideal reference source for researchers, graduate students, academicians, librarians, scholars, and industry-leading experts around the globe. |
| ethical issues in data science: Digital Witness Sam Dubberley, Alexa Koenig, Daragh Murray, 2020 This book covers the developing field of open source research and discusses how to use social media, satellite imagery, big data analytics, and user-generated content to strengthen human rights research and investigations. The topics are presented in an accessible format through extensive use of images and data visualization. |
| ethical issues in 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. |
| ethical issues in data science: Ethical Reasoning in Big Data Jeff Collmann, Sorin Adam Matei, 2016-04-22 This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public’s trust in the abilities of particular scientists and scientific institutions constitutes one of this book’s major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest – sometimes implicitly – a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging. This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Lastly, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature. |
| ethical issues in data science: Group Privacy Linnet Taylor, Luciano Floridi, Bart van der Sloot, 2016-12-28 The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies. |
| ethical issues in data science: Secondary Data Analysis Kali H. Trzesniewski, M. Brent Donnellan, Richard Eric Lucas, 2011 This wide-ranging yet practical book shows how the analysis of secondary data can provide unique opportunities for advancing psychological science. --Book Jacket. |
| ethical issues in data science: Ethical Choices in Research Harris M. Cooper, 2016 Many books discuss the ethical treatment of human subjects in behavioral research, yet few talk about the equally important ethical issues that arise when the data are being analyzed and the study is being written up. All researchers need to be aware of their professional responsibilities and make sound choices after the subjects have left. This practical and easy-to-follow guide walks readers through often overlooked decision points in the research process. Drawing from his extensive experience as a teacher of research methods and a senior editorial advisor, and from well-established standards of practice -- including the APA Ethics Code -- Harris Cooper is the ideal mentor in this process. Readers of this book will learn how to: Collect and manage data in a way that does not compromise the confidentiality of subjects Avoid data fraud and misleading data analysis Assign research responsibilities and authorships to team members Avoid committing plagiarism and intellectual theft Navigate the journal submission and publication process Post-publication ethical considerations are also addressed, including researchers' obligations when communicating their findings to the media and the general public, and when engaging with the scientific community as a peer reviewer. |
| ethical issues in data science: Handbook of Research Ethics and Scientific Integrity Ron Iphofen, 2020-04-02 This handbook is a ‘one-stop shop’ for current information, issues and challenges in the fields of research ethics and scientific integrity. It provides a comprehensive coverage of research and integrity issues, both within researchers’ ‘home’ discipline and in relation to similar concerns in other disciplines. The handbook covers common elements shared by disciplines and research professions, such as consent, privacy, data management, fraud, and plagiarism. The handbook also includes contributions and perspectives from academics from various disciplines, treating issues specific to their fields. Readers are able to quickly source the most comprehensive and up-to-date information, protagonists, issues and challenges in the field. Experienced researchers keen to assess their own perspectives, as well as novice researchers aiming to establish the field, will equally find the handbook of interest and practical benefit. It saves them a great deal of time in sourcing the disparate available material in these fields and it is the first ‘port of call’ for a wide range of researchers, research advisors, funding agencies and research reviewers.The most important feature is the handbook’s ability to provide practical advice and guidance to researchers in a wide range of disciplines and professions to help them ‘think through’ their approach to difficult questions related to the principles, values and standards they need to bring to their research practice. |
| ethical issues in data science: Innovation Management in the Intelligent World Tugrul U. Daim, Dirk Meissner, 2020-12-17 This book introduces readers to state-of-the-art cases and tools for managing innovation in today’s rapidly changing business environment. It provides a wealth of methodological knowhow and guidance on practical applications, as well as case studies that reveal various challenges in technology and innovation management. Written by a mix of academic scholars and practitioners, the respective chapters present tools and approaches for the early detection of emerging fields of innovation, as well as relevant processes and resources. The contributing authors hail from leading innovative companies including Google, Amazon, Intel, Daimler-Benz, and NASA. |
| ethical issues in data science: Improving Access to and Confidentiality of Research Data National Research Council, Commission on Behavioral and Social Sciences and Education, Committee on National Statistics, 2000-09-11 Improving Access to and Confidentiality of Research Data summarizes a workshop convened by the Committee on National Statistics (CNSTAT) to promote discussion about methods for advancing the often conflicting goals of exploiting the research potential of microdata and maintaining acceptable levels of confidentiality. This report outlines essential themes of the access versus confidentiality debate that emerged during the workshop. Among these themes are the tradeoffs and tensions between the needs of researchers and other data users on the one hand and confidentiality requirements on the other; the relative advantages and costs of data perturbation techniques (applied to facilitate public release) versus restricted access as tools for improving security; and the need to quantify disclosure risksâ€both absolute and relativeâ€created by researchers and research data, as well as by other data users and other types of data. |
| ethical issues in 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. |
| ethical issues in data science: The Ethics of Information Luciano Floridi, 2013-10 Luciano Floridi develops the first ethical framework for dealing with the new challenges posed by Information and Communication Technologies (ICTs). He establishes the conceptual foundations of Information Ethics by exploring important metatheoretical and introductory issues, and answering key theoretical questions of great philosophical interest. |
| ethical issues in data science: The Belmont Report United States. National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1978 |
ETHICAL Definition & Meaning - Merriam-Webster
The meaning of ETHICAL is of or relating to ethics. How to use ethical in a sentence. Synonym Discussion of Ethical.
ETHICAL | English meaning - Cambridge Dictionary
ETHICAL definition: 1. relating to beliefs about what is morally right and wrong: 2. morally right: 3. An ethical…. Learn more.
ETHICAL Definition & Meaning | Dictionary.com
Ethical definition: pertaining to or dealing with morals or the principles of morality; pertaining to right and wrong in conduct.. See examples of ETHICAL used in a sentence.
Ethics | Definition, History, Examples, Types, Philosophy, & Facts ...
Apr 21, 2025 · The term ethics may refer to the philosophical study of the concepts of moral right and wrong and moral good and bad, to any philosophical theory of what is morally right and …
Ethical - definition of ethical by The Free Dictionary
1. pertaining to or dealing with morals or the principles of morality; pertaining to ethics. 2. being in accordance with the rules or standards for right conduct or practice, esp. the standards of a …
ethical adjective - Definition, pictures, pronunciation and usage …
Definition of ethical adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
What does Ethical mean? - Definitions.net
Ethical refers to principles of right or wrong that govern a person's behavior or the conducting of an activity. It pertains to accepted standards of conduct based on concepts of morality, …
What Does Ethical Mean? | Clear Principles Explained
Ethical refers to principles that govern behavior, ensuring actions align with moral values and societal norms. Ethics is a branch of philosophy that deals with questions about what is …
ethical - Wiktionary, the free dictionary
May 15, 2025 · ethical (comparative more ethical, superlative most ethical) (philosophy, not comparable) Of or relating to the study of ethics. The philosopher Kant is particularly known for …
What Does Ethical Mean? - The Word Counter
Apr 2, 2022 · According to Dictionary, the word ethical is an adjective that means related to morals or principles or the concept of right and wrong. If something is ethical, it is within moral …
ETHICAL Definition & Meaning - Merriam-Webster
The meaning of ETHICAL is of or relating to ethics. How to use ethical in a sentence. Synonym Discussion of Ethical.
ETHICAL | English meaning - Cambridge Dictionary
ETHICAL definition: 1. relating to beliefs about what is morally right and wrong: 2. morally right: 3. An ethical…. Learn more.
ETHICAL Definition & Meaning | Dictionary.com
Ethical definition: pertaining to or dealing with morals or the principles of morality; pertaining to right and wrong in conduct.. See examples of ETHICAL used in a sentence.
Ethics | Definition, History, Examples, Types, Philosophy, & Facts ...
Apr 21, 2025 · The term ethics may refer to the philosophical study of the concepts of moral right and wrong and moral good and bad, to any philosophical theory of what is morally right and …
Ethical - definition of ethical by The Free Dictionary
1. pertaining to or dealing with morals or the principles of morality; pertaining to ethics. 2. being in accordance with the rules or standards for right conduct or practice, esp. the standards of a …
ethical adjective - Definition, pictures, pronunciation and usage …
Definition of ethical adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
What does Ethical mean? - Definitions.net
Ethical refers to principles of right or wrong that govern a person's behavior or the conducting of an activity. It pertains to accepted standards of conduct based on concepts of morality, …
What Does Ethical Mean? | Clear Principles Explained
Ethical refers to principles that govern behavior, ensuring actions align with moral values and societal norms. Ethics is a branch of philosophy that deals with questions about what is …
ethical - Wiktionary, the free dictionary
May 15, 2025 · ethical (comparative more ethical, superlative most ethical) (philosophy, not comparable) Of or relating to the study of ethics. The philosopher Kant is particularly known for …
What Does Ethical Mean? - The Word Counter
Apr 2, 2022 · According to Dictionary, the word ethical is an adjective that means related to morals or principles or the concept of right and wrong. If something is ethical, it is within moral …
