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martin kirsten - ethics of data and analytics

Ethics of Data and Analytics Concepts and Cases




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Genere:Libro
Lingua: Inglese
Pubblicazione: 05/2022
Edizione: 1° edizione





Note Editore

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.




Sommario

1 Value-Laden Biases in Data Analytics1.1 This Is the Stanford Vaccine Algorithm That Left out Frontline DoctorsEileen Guo And Karen Hao1.2 Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black PatientsCarolyn Y. Johnson1.3 Excerpt from Do Artifacts Have Politics?Langdon Winner1.4 Excerpt from Bias in Computer SystemsBatya Friedman And Helen Nissenbaum1.5 Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine LearningGabbrielle M. Johnson1.6 Algorithmic Bias and Corporate Responsibility: How Companies Hide behind the False Veil of the Technological ImperativeKirsten Martin 2 Ethical Theories and Data Analytics2.1 Language Models Like GPT-3 Could Herald a New Type of Search EngineWill Douglas Heaven2.2 How to Make a Chatbot That Isn’t Racist or SexistWill Douglas Heaven2.3 This Facial Recognition Website Can Turn Anyone into a Cop—or a StalkerDrew Harwell2.4 Excerpt from Technology and the Virtues: A Philosophical Guide to a Future Worth WantingShannon Vallor2.5 Ethics of Care as Moral Grounding for AICarolina Villegas-Galaviz2.6 Excerpt from Operationalizing Critical Race Theory in the MarketplaceSonja Martin Poole, Sonya A. Grier, Kevin D. Thomas, Francesca Sobande, Akon E. Ekpo, Lez Trujillo Torres, Lynn A. Addington, Melinda Weekes-Laidlow, And Geraldine Rosa Henderson 3 Privacy, Data, and Shared Responsibility3.1 Finding Consumers, No Matter Where They Hide: Ad Targeting and Location DataKirsten Martin3.2 How a Company You’ve Never Heard of Sends You Letters about Your Medical ConditionSurya Mattu And Kashmir Hill3.3 Excerpt from A Contextual Approach to Privacy OnlineHelen Nissenbaum3.4 Excerpt from Understanding Privacy Online: Development of a Social Contract Approach to PrivacyKirsten Martin3.5 Privacy Law for Business Decision-Makers in the United StatesClarissa Wilbur Berger3.6 Wrongfully Accused by an AlgorithmKashmir Hill3.7 Facial Recognition Is Accurate, If You’re a White GuySteve Lohr3.8 Excerpt from Datasheets for DatasetsTimnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford 4 Surveillance and Power4.1 Twelve Million Phones, One Dataset, Zero PrivacyStuart A. Thompson and Charlie Warzel4.2 The Secretive Company That Might End Privacy as We Know ItKashmir Hill4.3 Excerpt from Big Brother to Electronic PanopticonDavid Lyon4.4 Excerpt from Privacy, Visibility, Transparency, and ExposureJulie E. Cohen 5 The Purpose of the Corporation and Data Analytics5.1 The Quiet Growth of Race-Detection Software Sparks Concerns over BiasParmy Olson5.2 A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the JobDrew Harwell5.3 Excerpt from Managing for StakeholdersR. Edward Freeman5.4 Excerpt from The Problem of Corporate PurposeLynn A. Stout5.5 Recommending an Insurrection: Facebook and Recommendation AlgorithmsKirsten Martin5.6 Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment?Robert H. Frank 6 Fairness and Justice in Data Analytics6.1 Machine BiasJulia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner6.2 Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers SayJulia Angwin and Jeff Larson6.3 Major Universities Are Using Race as a "High Impact Predictor" of Student SuccessTodd Feathers6.4 Excerpt from Distributive JusticeRobert Nozick6.5 Excerpt from Justice as FairnessJohn Rawls6.6 Excerpt from Tyranny and Complex EqualityMichael Walzer 7 Discrimination and Data Analytics7.1 Amazon Scraps Secret AI Recruiting Tool that Showed Bias against WomenJeffrey Dastin7.2 Bias Isn’t the Only Problem with Credit Scores—and No, AI Can’t HelpWill Douglas Heaven7.3 Excerpt from Big Data’s Disparate ImpactSolon Barocas and Andrew D. Selbst7.4 Excerpt from Where Fairness Fails: Data, Algorithms, and the Limits of Antidiscrimination DiscourseAnna Lauren Hoffman 8 Creating Outcomes and Accuracy in Data Analytics8.1 Pasco’s Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals: The Kids and Their Parents Don’t KnowNeil Bedi and Kathleen Mcgory8.2 Excerpt from Reliance on Metrics is a Fundamental Challenge for AIRachel L. Thomas and David Uminsky8.3 Excerpt from Designing Ethical AlgorithmsKirsten Martin 9 Gamification, Manipulation, and Data Analytics9.1 How Uber Uses Psychological Tricks to Push Its Drivers’ ButtonsNoam Scheiber9.2 How Deepfakes Could Change Fashion AdvertisingKati Chitrakorn9.3 Excerpt from Ethics of GamificationTae Wan Kim and Kevin Werbach9.4 Excerpt from Manipulation, Privacy, and ChoiceKirsten Martin9.5 Excerpt from Ethics of the Attention Economy: The Problem of Social Media AddictionVikram R. Bhargava and Manuel Velasquez 10 Transparency and Accountability in Data Analytics10.1 Houston Teachers to Pursue Lawsuit over Secret Evaluation SystemShelby Webb10.2 Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting backDrew Harwell10.3 When Algorithms Mess Up, the Nearest Human Gets the BlameKaren Hao10.4 Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the ProfessionsDaniel N. Kluttz, Nitin Kohli, and Deirdre K. Mulligan 11 Ethics, AI, Research, and Corporations11.1 Google Research: Who Is Responsible for Ethics of AI?Kirsten Martin11.2 The Scientist Qua Scientist Makes Value JudgmentsRichard Rudner11.3 Excerpt from Ethical Implications and Accountability of AlgorithmsKirsten Martin




Autore

Kirsten Martin is the William P. and Hazel B. White Center Professor of Technology Ethics at the University of Notre Dame’s Mendoza College of Business. A professor in the IT, Analytics, and Operations department but focus on the ethics of data and analytics, she has been teaching business ethics in a business school for 15 years and has experience writing and teaching on the ethics of data, analytics and privacy. Her research focuses on privacy, accountability, technology, algorithms, and ethics Martin is the editor of the "Technology and Business Ethics" section in the Journal of Business Ethics. She is the coauthor of a recent book on business ethics for the popular press (The Power of And) and has a popular Ted talk on privacy and data. She holds Ph.D. and MBA degrees from the University of Virginia’s Darden School of Business and a B.S. Engineering is from the University of Michigan.










Altre Informazioni

ISBN:

9781032217314

Condizione: Nuovo
Dimensioni: 10 x 7 in Ø 1.00 lb
Formato: Copertina rigida
Illustration Notes:40 b/w images and 40 line drawings
Pagine Arabe: 474
Pagine Romane: xviii


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