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.

chapter 1|5 pages

Value-Laden Biases in Data Analytics

ByKirsten Martin

chapter Chapter 1.1|4 pages

This Is the Stanford Vaccine Algorithm That Left out Frontline Doctors *

ByEileen Guo, Karen Hao

chapter Chapter 1.2|3 pages

Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black Patients *

ByCarolyn Y. Johnson

chapter Chapter 1.3|7 pages

Excerpt from Do Artifacts Have Politics? *

ByLangdon Winner

chapter Chapter 1.4|7 pages

Excerpt from Bias in Computer Systems *

ByBatya Friedman, Helen Nissenbaum

chapter Chapter 1.5|9 pages

Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning *

ByGabbrielle M. Johnson

chapter 2|6 pages

Ethical Theories and Data Analytics

ByKirsten Martin

chapter Chapter 2.1|3 pages

Language Models Like GPT-3 Could Herald a New Type of Search Engine *

ByWill Douglas Heaven

chapter Chapter 2.2|3 pages

How to Make a Chatbot That Isn't Racist or Sexist *

ByWill Douglas Heaven

chapter Chapter 2.3|5 pages

This Facial Recognition Website Can Turn Anyone into a Cop—or a Stalker *

ByDrew Harwell

chapter Chapter 2.5|6 pages

Ethics of Care as Moral Grounding for AI *

ByCarolina Villegas-Galaviz

chapter Chapter 2.6|9 pages

Excerpt from Operationalizing Critical Race Theory in the Marketplace *

BySonja Martin Poole, Sonya A. Grier, Kevin D. Thomas, Francesca Sobande, Akon E. Ekpo, Lez Trujillo Torres, Lynn A. Addington, Melinda Weekes-Laidlow, Geraldine Rosa Henderson

chapter 3|6 pages

Privacy, Data, and Shared Responsibility

ByKirsten Martin

chapter Chapter 3.1|8 pages

Finding Consumers, No Matter Where They Hide: Ad Targeting and Location Data *

ByKirsten Martin

chapter Chapter 3.2|5 pages

How a Company You've Never Heard of Sends You Letters about Your Medical Condition *

BySurya Mattu, Kashmir Hill

chapter Chapter 3.3|7 pages

Excerpt from A Contextual Approach to Privacy Online *

ByHelen Nissenbaum

chapter Chapter 3.5|9 pages

Privacy Law for Business Decision-Makers in the United States

ByClarissa Wilbur Berger

chapter Chapter 3.6|5 pages

Wrongfully Accused by an Algorithm *

ByKashmir Hill

chapter Chapter 3.7|5 pages

Facial Recognition Is Accurate, If You're a White Guy *

BySteve Lohr

chapter Chapter 3.8|9 pages

Excerpt from Datasheets for Datasets *

ByTimnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé, Kate Crawford

chapter 4|4 pages

Surveillance and Power

ByKirsten Martin

chapter Chapter 4.1|9 pages

Twelve Million Phones, One Dataset, Zero Privacy *

ByStuart A. Thompson, Charlie Warzel

chapter Chapter 4.2|8 pages

The Secretive Company That Might End Privacy as We Know It *

ByKashmir Hill

chapter Chapter 4.3|10 pages

Excerpt from Big Brother to Electronic Panopticon *

ByDavid Lyon

chapter Chapter 4.4|7 pages

Excerpt from Privacy, Visibility, Transparency, and Exposure *

ByJulie E. Cohen

chapter 5|6 pages

The Purpose of the Corporation and Data Analytics

ByKirsten Martin

chapter Chapter 5.1|5 pages

The Quiet Growth of Race-Detection Software Sparks Concerns over Bias *

ByParmy Olson

chapter Chapter 5.2|6 pages

A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the Job *

ByDrew Harwell

chapter Chapter 5.3|5 pages

Excerpt from Managing for Stakeholders *

ByR. Edward Freeman

chapter Chapter 5.4|8 pages

Excerpt from The Problem of Corporate Purpose *

ByLynn A. Stout

chapter Chapter 5.5|15 pages

Recommending an Insurrection: Facebook and Recommendation Algorithms *

ByKirsten Martin

chapter Chapter 5.6|9 pages

Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment? *

ByRobert H. Frank

chapter 6|5 pages

Fairness and Justice in Data Analytics

ByKirsten Martin

chapter Chapter 6.1|11 pages

Machine Bias *

ByJulia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner

chapter Chapter 6.2|3 pages

Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say *

ByJulia Angwin, Jeff Larson

chapter Chapter 6.4|3 pages

Excerpt from Distributive Justice *

ByRobert Nozick

chapter Chapter 6.5|5 pages

Excerpt from Justice as Fairness *

ByJohn Rawls

chapter Chapter 6.6|9 pages

Excerpt from Tyranny and Complex Equality *

ByMichael Walzer

chapter 7|5 pages

Discrimination and Data Analytics

ByKirsten Martin

chapter Chapter 7.1|4 pages

Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women *

ByJeffrey Dastin

chapter Chapter 7.2|3 pages

Bias Isn't the Only Problem with Credit Scores—and No, AI Can't Help *

ByWill Douglas Heaven

chapter Chapter 7.3|16 pages

Excerpt from Big Data's Disparate Impact *

BySolon Barocas, Andrew D. Selbst

chapter 8|6 pages

Creating Outcomes and Accuracy in Data Analytics

ByKirsten Martin

chapter Chapter 8.1|7 pages

Pasco's Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals:

The Kids and Their Parents Don't Know *
ByNeil Bedi, Kathleen McGory

chapter Chapter 8.2|8 pages

Excerpt from Reliance on Metrics is a Fundamental Challenge for AI *

ByRachel L. Thomas, David Uminsky

chapter Chapter 8.3|7 pages

Excerpt from Designing Ethical Algorithms *

ByKirsten Martin

chapter 9|5 pages

Gamification, Manipulation, and Data Analytics

ByKirsten Martin

chapter Chapter 9.1|10 pages

How Uber Uses Psychological Tricks to Push Its Drivers' Buttons *

ByNoam Scheiber

chapter Chapter 9.2|3 pages

How Deepfakes Could Change Fashion Advertising *

ByKati Chitrakorn

chapter Chapter 9.3|11 pages

Excerpt from Ethics of Gamification *

ByTae Wan Kim, Kevin Werbach

chapter Chapter 9.4|5 pages

Excerpt from Manipulation, Privacy, and Choice *

ByKirsten Martin

chapter Chapter 9.5|12 pages

Excerpt from Ethics of the Attention Economy: The Problem of Social Media Addiction *

ByVikram R. Bhargava, Manuel Velasquez

chapter 10|5 pages

Transparency and Accountability in Data Analytics

ByKirsten Martin

chapter Chapter 10.1|2 pages

Houston Teachers to Pursue Lawsuit over Secret Evaluation System *

ByShelby Webb

chapter Chapter 10.3|2 pages

When Algorithms Mess Up, the Nearest Human Gets the Blame *

ByKaren Hao

chapter Chapter 10.4|9 pages

Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions *

ByDaniel N. Kluttz, Nitin Kohli, Deirdre K. Mulligan

chapter 11|5 pages

Ethics, AI, Research, and Corporations

ByKirsten Martin

chapter Chapter 11.1|13 pages

Google Research: Who Is Responsible for Ethics of AI? *

ByKirsten Martin

chapter Chapter 11.2|6 pages

The Scientist Qua Scientist Makes Value Judgments *

ByRichard Rudner

chapter Chapter 11.3|10 pages

Excerpt from Ethical Implications and Accountability of Algorithms *

ByKirsten Martin