1st Edition

Ethics of Data and Analytics Concepts and Cases

By Kirsten Martin Copyright 2022
    492 Pages 40 B/W Illustrations
    by Auerbach Publications

    492 Pages 40 B/W Illustrations
    by Auerbach Publications

    492 Pages 40 B/W Illustrations
    by Auerbach Publications

    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.

    1 Value-Laden Biases in Data Analytics
    1.1 This Is the Stanford Vaccine Algorithm That Left out Frontline Doctors
    Eileen Guo And Karen Hao
    1.2 Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black Patients
    Carolyn Y. Johnson
    1.3 Excerpt from Do Artifacts Have Politics?
    Langdon Winner
    1.4 Excerpt from Bias in Computer Systems
    Batya Friedman And Helen Nissenbaum
    1.5 Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning
    Gabbrielle M. Johnson
    1.6 Algorithmic Bias and Corporate Responsibility: How Companies Hide behind the False Veil of the Technological Imperative
    Kirsten Martin

    2 Ethical Theories and Data Analytics
    2.1 Language Models Like GPT-3 Could Herald a New Type of Search Engine
    Will Douglas Heaven
    2.2 How to Make a Chatbot That Isn’t Racist or Sexist
    Will Douglas Heaven
    2.3 This Facial Recognition Website Can Turn Anyone into a Cop—or a Stalker
    Drew Harwell
    2.4 Excerpt from Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting
    Shannon Vallor
    2.5 Ethics of Care as Moral Grounding for AI
    Carolina Villegas-Galaviz
    2.6 Excerpt from Operationalizing Critical Race Theory in the Marketplace
    Sonja 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 Responsibility
    3.1 Finding Consumers, No Matter Where They Hide: Ad Targeting and Location Data
    Kirsten Martin
    3.2 How a Company You’ve Never Heard of Sends You Letters about Your Medical Condition
    Surya Mattu And Kashmir Hill
    3.3 Excerpt from A Contextual Approach to Privacy Online
    Helen Nissenbaum
    3.4 Excerpt from Understanding Privacy Online: Development of a Social Contract Approach to Privacy
    Kirsten Martin
    3.5 Privacy Law for Business Decision-Makers in the United States
    Clarissa Wilbur Berger
    3.6 Wrongfully Accused by an Algorithm
    Kashmir Hill
    3.7 Facial Recognition Is Accurate, If You’re a White Guy
    Steve Lohr
    3.8 Excerpt from Datasheets for Datasets
    Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford

    4 Surveillance and Power
    4.1 Twelve Million Phones, One Dataset, Zero Privacy
    Stuart A. Thompson and Charlie Warzel
    4.2 The Secretive Company That Might End Privacy as We Know It
    Kashmir Hill
    4.3 Excerpt from Big Brother to Electronic Panopticon
    David Lyon
    4.4 Excerpt from Privacy, Visibility, Transparency, and Exposure
    Julie E. Cohen

    5 The Purpose of the Corporation and Data Analytics
    5.1 The Quiet Growth of Race-Detection Software Sparks Concerns over Bias
    Parmy Olson
    5.2 A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the Job
    Drew Harwell
    5.3 Excerpt from Managing for Stakeholders
    R. Edward Freeman
    5.4 Excerpt from The Problem of Corporate Purpose
    Lynn A. Stout
    5.5 Recommending an Insurrection: Facebook and Recommendation Algorithms
    Kirsten Martin
    5.6 Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment?
    Robert H. Frank

    6 Fairness and Justice in Data Analytics
    6.1 Machine Bias
    Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner
    6.2 Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say
    Julia Angwin and Jeff Larson
    6.3 Major Universities Are Using Race as a "High Impact Predictor" of Student Success
    Todd Feathers
    6.4 Excerpt from Distributive Justice
    Robert Nozick
    6.5 Excerpt from Justice as Fairness
    John Rawls
    6.6 Excerpt from Tyranny and Complex Equality
    Michael Walzer

    7 Discrimination and Data Analytics
    7.1 Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women
    Jeffrey Dastin
    7.2 Bias Isn’t the Only Problem with Credit Scores—and No, AI Can’t Help
    Will Douglas Heaven
    7.3 Excerpt from Big Data’s Disparate Impact
    Solon Barocas and Andrew D. Selbst
    7.4 Excerpt from Where Fairness Fails: Data, Algorithms, and the Limits of Antidiscrimination Discourse
    Anna Lauren Hoffman

    8 Creating Outcomes and Accuracy in Data Analytics
    8.1 Pasco’s Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals: The Kids and Their Parents Don’t Know
    Neil Bedi and Kathleen Mcgory
    8.2 Excerpt from Reliance on Metrics is a Fundamental Challenge for AI
    Rachel L. Thomas and David Uminsky
    8.3 Excerpt from Designing Ethical Algorithms
    Kirsten Martin

    9 Gamification, Manipulation, and Data Analytics
    9.1 How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons
    Noam Scheiber
    9.2 How Deepfakes Could Change Fashion Advertising
    Kati Chitrakorn
    9.3 Excerpt from Ethics of Gamification
    Tae Wan Kim and Kevin Werbach
    9.4 Excerpt from Manipulation, Privacy, and Choice
    Kirsten Martin
    9.5 Excerpt from Ethics of the Attention Economy: The Problem of Social Media Addiction
    Vikram R. Bhargava and Manuel Velasquez

    10 Transparency and Accountability in Data Analytics
    10.1 Houston Teachers to Pursue Lawsuit over Secret Evaluation System
    Shelby Webb
    10.2 Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting back
    Drew Harwell
    10.3 When Algorithms Mess Up, the Nearest Human Gets the Blame
    Karen Hao
    10.4 Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions
    Daniel N. Kluttz, Nitin Kohli, and Deirdre K. Mulligan

    11 Ethics, AI, Research, and Corporations
    11.1 Google Research: Who Is Responsible for Ethics of AI?
    Kirsten Martin
    11.2 The Scientist Qua Scientist Makes Value Judgments
    Richard Rudner
    11.3 Excerpt from Ethical Implications and Accountability of Algorithms
    Kirsten Martin


    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.