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
Biography
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.
