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Real World Ai Ethics For Data Scientists
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Book Synopsis Real World AI Ethics for Data Scientists by : Nachshon (Sean) Goltz
Download or read book Real World AI Ethics for Data Scientists written by Nachshon (Sean) Goltz and published by CRC Press. This book was released on 2023-04-13 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the midst of the fourth industrial revolution, big data is weighed in gold, placing enormous power in the hands of data scientists – the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape, in a practical, diverse, and inclusive way, the ethical compass of those entrusted with big data. Being practical, this book provides seven real-world case studies dealing with big data abuse. These cases span a range of topics from the statistical manipulation of research in the Cornell food lab through the Facebook user data abuse done by Cambridge Analytica to the abuse of farm animals by AI in a chapter co-authored by renowned philosophers Peter Singer and Yip Fai Tse. Diverse and inclusive, given the global nature of this revolution, this book provides case-by-case commentary on the cases by scholars representing non-Western ethical approaches (Buddhist, Jewish, Indigenous, and African) as well as Western approaches (consequentialism, deontology, and virtue). We hope this book will be a lighthouse for those debating ethical dilemmas in this challenging and ever-evolving field.
Book Synopsis Ethics and Data Science by : Mike Loukides
Download or read book Ethics and Data Science written by Mike Loukides and published by "O'Reilly Media, Inc.". This book was released on 2018-07-25 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
Book Synopsis 97 Things About Ethics Everyone in Data Science Should Know by : Bill Franks
Download or read book 97 Things About Ethics Everyone in Data Science Should Know written by Bill Franks and published by O'Reilly Media. This book was released on 2020-08-06 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most of the high-profile cases of real or perceived unethical activity in data science aren’t matters of bad intent. Rather, they occur because the ethics simply aren’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 Concept—Tim Wilson How to Approach Ethical Transparency—Rado Kotorov Unbiased ≠ Fair—Doug Hague Rules and Rationality—Christof Wolf Brenner The Truth About AI Bias—Cassie Kozyrkov Cautionary Ethics Tales—Sherrill Hayes Fairness in the Age of Algorithms—Anna Jacobson The Ethical Data Storyteller—Brent Dykes Introducing Ethicize™, the Fully AI-Driven Cloud-Based Ethics Solution!—Brian O’Neill Be Careful with "Decisions of the Heart"—Hugh Watson Understanding Passive Versus Proactive Ethics—Bill Schmarzo
Book Synopsis Data Science Ethics by : David Martens
Download or read book Data Science Ethics written by David Martens and published by Oxford University Press. This book was released on 2022-03-24 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data.
Book Synopsis Data Science Ethics by : David Martens
Download or read book Data Science Ethics written by David Martens and published by Oxford University Press. This book was released on 2022-03-24 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science ethics is all about what is right and wrong when conducting data science. Data science has so far been primarily used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. While data scientists and business managers are not inherently unethical, they are not trained to weigh the ethical considerations that come from their work - Data Science Ethics addresses this increasingly significant gap and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques. Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data.
Book Synopsis Responsible Data Science by : Peter C. Bruce
Download or read book Responsible Data Science written by Peter C. Bruce and published by John Wiley & Sons. This book was released on 2021-04-13 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the most serious prevalent ethical issues in data science with this insightful new resource The increasing popularity of data science has resulted in numerous well-publicized cases of bias, injustice, and discrimination. The widespread deployment of “Black box” algorithms that are difficult or impossible to understand and explain, even for their developers, is a primary source of these unanticipated harms, making modern techniques and methods for manipulating large data sets seem sinister, even dangerous. When put in the hands of authoritarian governments, these algorithms have enabled suppression of political dissent and persecution of minorities. To prevent these harms, data scientists everywhere must come to understand how the algorithms that they build and deploy may harm certain groups or be unfair. Responsible Data Science delivers a comprehensive, practical treatment of how to implement data science solutions in an even-handed and ethical manner that minimizes the risk of undue harm to vulnerable members of society. Both data science practitioners and managers of analytics teams will learn how to: Improve model transparency, even for black box models Diagnose bias and unfairness within models using multiple metrics Audit projects to ensure fairness and minimize the possibility of unintended harm Perfect for data science practitioners, Responsible Data Science will also earn a spot on the bookshelves of technically inclined managers, software developers, and statisticians.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309475597 Total Pages :139 pages Book Rating :4.3/5 (94 download)
Book Synopsis Data Science for Undergraduates by : National Academies of Sciences, Engineering, and Medicine
Download or read book Data Science for Undergraduates written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-11-11 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
Book Synopsis Ethics of Artificial Intelligence by : Bernd Carsten Stahl
Download or read book Ethics of Artificial Intelligence written by Bernd Carsten Stahl and published by Springer Nature. This book was released on 2022-11-01 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives. Given the omnipresence of AI ethics in academic, policy and media debates, the book will be suitable for a wide range of audiences, from scholars of different disciplines (e.g. AI science, ethics, politics, philosophy, economics) to policy-makers, lobbying NGOs, teachers and the educated public.
Book Synopsis The Ethical Frontier of AI and Data Analysis by : Kumar, Rajeev
Download or read book The Ethical Frontier of AI and Data Analysis written by Kumar, Rajeev and published by IGI Global. This book was released on 2024-03-04 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the advancing fields of artificial intelligence (AI) and data science, a pressing ethical dilemma arises. As technology continues its relentless march forward, ethical considerations within these domains become increasingly complex and critical. Bias in algorithms, lack of transparency, data privacy breaches, and the broader societal repercussions of AI applications are demanding urgent attention. This ethical quandary poses a formidable challenge for researchers, academics, and industry professionals alike, threatening the very foundation of responsible technological innovation. Navigating this ethical minefield requires a comprehensive understanding of the multifaceted issues at hand. The Ethical Frontier of AI and Data Analysis is an indispensable resource crafted to address the ethical challenges that define the future of AI and data science. Researchers and academics who find themselves at the forefront of this challenge are grappling with the evolving landscape of AI and data science ethics. Underscoring the need for this book is the current lack of clarity on ethical frameworks, bias mitigation strategies, and the broader societal implications, which hinder progress and leave a void in the discourse. As the demand for responsible AI solutions intensifies, the imperative for this reliable guide that consolidates, explores, and advances the dialogue on ethical considerations grows exponentially.
Book Synopsis Improving Equity in Data Science by : Colby Tofel-Grehl
Download or read book Improving Equity in Data Science written by Colby Tofel-Grehl and published by Taylor & Francis. This book was released on 2024-06-03 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms.
Book Synopsis The Decision Maker's Handbook to Data Science by : Stylianos Kampakis
Download or read book The Decision Maker's Handbook to Data Science written by Stylianos Kampakis and published by Apress. This book was released on 2024-07-15 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. This third edition delves into the latest advancements in AI, particularly focusing on large language models (LLMs), with clear distinctions made between AI and traditional data science, including AI's ability to emulate human decision-making. Author Stylianos Kampakis introduces you to the critical aspect of ethics in AI, an area of growing importance and scrutiny. The narrative examines the ethical considerations intrinsic to the development and deployment of AI technologies, including bias, fairness, transparency, and accountability. You’ll be provided with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated edition also includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists. Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Integrate AI with other innovative technologies Explore anticipated ethical, regulatory, and technical landscapes that will shape the future of AI and data science Discover how to hire and manage data scientists Build the right environment in order to make your organization data-driven Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.
Book Synopsis ETHICAL FRONTIER OF AI AND DATA ANALYSIS. by :
Download or read book ETHICAL FRONTIER OF AI AND DATA ANALYSIS. written by and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book AI Ethics written by Mark Coeckelbergh and published by MIT Press. This book was released on 2020-04-07 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This overview of the ethical issues raised by artificial intelligence moves beyond hype and nightmare scenarios to address concrete questions—offering a compelling, necessary read for our ChatGPT era. Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the usual hype and nightmare scenarios to address concrete questions. Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein’s monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society.
Book Synopsis Guide to Teaching Data Science by : Orit Hazzan
Download or read book Guide to Teaching Data Science written by Orit Hazzan and published by Springer Nature. This book was released on 2023-03-20 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.
Book Synopsis Counting Feminicide by : Catherine D'Ignazio
Download or read book Counting Feminicide written by Catherine D'Ignazio and published by MIT Press. This book was released on 2024-04-30 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why grassroots data activists in Latin America count feminicide—and how this vital social justice work challenges mainstream data science. What isn’t counted doesn’t count. And mainstream institutions systematically fail to account for feminicide, the gender-related killing of women and girls, including cisgender and transgender women. Against this failure, Counting Feminicide brings to the fore the work of data activists across the Americas who are documenting such murders—and challenging the reigning logic of data science by centering care, memory, and justice in their work. Drawing on Data Against Feminicide, a large-scale collaborative research project, Catherine D’Ignazio describes the creative, intellectual, and emotional labor of feminicide data activists who are at the forefront of a data ethics that rigorously and consistently takes power and people into account. Individuals, researchers, and journalists—these data activists scour news sources to assemble spreadsheets and databases of women killed by gender-related violence, then circulate those data in a variety of creative and political forms. Their work reveals the potential of restorative/transformative data science—the use of systematic information to, first, heal communities from the violence and trauma produced by structural inequality and, second, envision and work toward the world in which such violence has been eliminated. Specifically, D’Ignazio explores the possibilities and limitations of counting and quantification—reducing complex social phenomena to convenient, sortable, aggregable forms—when the goal is nothing short of the elimination of gender-related violence. Counting Feminicide showcases the incredible power of data feminism in practice, in which each murdered woman or girl counts, and, in being counted, joins a collective demand for the restoration of rights and a transformation of the gendered order of the world.
Book Synopsis Ultimate Azure Data Scientist Associate (DP-100) Certification Guide by : Rajib Kumar De
Download or read book Ultimate Azure Data Scientist Associate (DP-100) Certification Guide written by Rajib Kumar De and published by Orange Education Pvt Ltd. This book was released on 2024-06-26 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: TAGLINE Empower Your Data Science Journey: From Exploration to Certification in Azure Machine Learning KEY FEATURES ● Offers deep dives into key areas such as data preparation, model training, and deployment, ensuring you master each concept. ● Covers all exam objectives in detail, ensuring a thorough understanding of each topic required for the DP-100 certification. ● Includes hands-on labs and practical examples to help you apply theoretical knowledge to real-world scenarios, enhancing your learning experience. DESCRIPTION Ultimate Azure Data Scientist Associate (DP-100) Certification Guide is your essential resource for achieving the Microsoft Azure Data Scientist Associate certification. This guide covers all exam objectives, helping you design and prepare machine learning solutions, explore data, train models, and manage deployment and retraining processes. The book starts with the basics and advances through hands-on exercises and real-world projects, to help you gain practical experience with Azure's tools and services. The book features certification-oriented Q&A challenges that mirror the actual exam, with detailed explanations to help you thoroughly grasp each topic. Perfect for aspiring data scientists, IT professionals, and analysts, this comprehensive guide equips you with the expertise to excel in the DP-100 exam and advance your data science career. WHAT WILL YOU LEARN ● Design and prepare effective machine learning solutions in Microsoft Azure. ● Learn to develop complete machine learning training pipelines, with or without code. ● Explore data, train models, and validate ML pipelines efficiently. ● Deploy, manage, and optimize machine learning models in Azure. ● Utilize Azure's suite of data science tools and services, including Prompt Flow, Model Catalog, and AI Studio. ● Apply real-world data science techniques to business problems. ● Confidently tackle DP-100 certification exam questions and scenarios. WHO IS THIS BOOK FOR? This book is for aspiring Data Scientists, IT Professionals, Developers, Data Analysts, Students, and Business Professionals aiming to Master Azure Data Science. Prior knowledge of basic Data Science concepts and programming, particularly in Python, will be beneficial for making the most of this comprehensive guide. TABLE OF CONTENTS 1. Introduction to Data Science and Azure 2. Setting Up Your Azure Environment 3. Data Ingestion and Storage in Azure 4. Data Transformation and Cleaning 5. Introduction to Machine Learning 6. Azure Machine Learning Studio 7. Model Deployment and Monitoring 8. Embracing AI Revolution Azure 9. Responsible AI and Ethics 10. Big Data Analytics with Azure 11. Real-World Applications and Case Studies 12. Conclusion and Next Steps Index
Book Synopsis Real World AI by : Alyssa Simpson Rochwerger
Download or read book Real World AI written by Alyssa Simpson Rochwerger and published by . This book was released on 2021-02-17 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can you successfully deploy AI? When AI works, it's nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren't random. This practical guide to deploying AI lays out a human-first, responsible approach that has seen more than three times the success rate when compared to the industry average. In Real World AI, Alyssa Simpson Rochwerger and Wilson Pang share dozens of AI stories from startups and global enterprises alike featuring personal experiences from people who have worked on global AI deployments that impact billions of people every day. AI for business doesn't have to be overwhelming. Real World AI uses plain language to walk you through an AI approach that you can feel confident about-for your business and for your customers.
