Ethics of Data and Analytics : Concepts and Cases

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 Cover Half Title Title Page Copyright Page Table of Contents Introduction Chapter 1 Value-Laden Biases in Data Analytics Summary of Readings Related Cases Notes Chapter 1.1 This Is the Stanford Vaccine Algorithm That Left out Frontline Doctors Chapter 1.2 Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black Patients Chapter 1.3 Excerpt from Do Artifacts Have Politics? Chapter 1.4 Excerpt from Bias in Computer Systems Chapter 1.5 Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning Chapter 1.6 Algorithmic Bias and Corporate Responsibility: How Companies Hide behind the False Veil of the Technological Imperative Chapter 2 Ethical Theories and Data Analytics Summary of Readings Virtue Ethics Critical Approaches, Ethics, and Power Related Cases Notes Chapter 2.1 Language Models Like GPT-3 Could Herald a New Type of Search Engine Chapter 2.2 How to Make a Chatbot That Isn’t Racist or Sexist Chapter 2.3 This Facial Recognition Website Can Turn Anyone into a Cop—or a Stalker Chapter 2.4 Excerpt from Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting Chapter 2.5 Ethics of Care as Moral Grounding for AI Chapter 2.6 Excerpt from Operationalizing Critical Race Theory in the Marketplace Chapter 3 Privacy, Data, and Shared Responsibility Summary of Readings - Privacy Related Cases - Privacy Summary of Readings - Questions for Data Related Cases - Questions for Data Notes Chapter 3.1 Finding Consumers, No Matter Where They Hide: Ad Targeting and Location Data Chapter 3.2 How a Company You’ve Never Heard of Sends You Letters about Your Medical Condition Chapter 3.3 Excerpt from A Contextual Approach to Privacy Online Chapter 3.4 Excerpt from Understanding Privacy Online: Development of a Social Contract Approach to Privacy Chapter 3.5 Privacy Law for Business Decision-Makers in the United States Chapter 3.6 Wrongfully Accused by an Algorithm Chapter 3.7 Facial Recognition Is Accurate, If You’re a White Guy Chapter 3.8 Excerpt from Datasheets for Datasets Chapter 4 Surveillance and Power Summary of Readings Related Cases Notes Chapter 4.1 Twelve Million Phones, One Dataset, Zero Privacy Chapter 4.2 The Secretive Company That Might End Privacy as We Know It Chapter 4.3 Excerpt from Big Brother to Electronic Panopticon Chapter 4.4 Excerpt from Privacy, Visibility, Transparency, and Exposure Chapter 5 The Purpose of the Corporation and Data Analytics Summary of Readings Related Cases Notes Chapter 5.1 The Quiet Growth of Race-Detection Software Sparks Concerns over Bias Chapter 5.2 A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the Job Chapter 5.3 Excerpt from Managing for Stakeholders Chapter 5.4 Excerpt from The Problem of Corporate Purpose Chapter 5.5 Recommending an Insurrection: Facebook and Recommendation Algorithms Chapter 5.6 Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment? Chapter 6 Fairness and Justice in Data Analytics Summary of Readings Related Cases Notes Chapter 6.1 Machine Bias Chapter 6.2 Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say Chapter 6.3 Major Universities Are Using Race as a “High Impact Predictor” of Student Success Chapter 6.4 Excerpt from Distributive Justice Chapter 6.5 Excerpt from Justice as Fairness Chapter 6.6 Excerpt from Tyranny and Complex Equality Chapter 7 Discrimination and Data Analytics Summary of Readings Related Cases Notes Chapter 7.1 Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women Chapter 7.2 Bias Isn’t the Only Problem with Credit Scores—and No, AI Can’t Help Chapter 7.3 Excerpt from Big Data’s Disparate Impact Chapter 7.4 Excerpt from Where Fairness Fails: Data, Algorithms, and the Limits of Antidiscrimination Discourse Chapter 8 Creating Outcomes and Accuracy in Data Analytics Summary of Readings Related Cases Notes Chapter 8.1 Pasco’s Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals: The Kids and Their Parents Don’t Know Chapter 8.2 Excerpt from Reliance on Metrics is a Fundamental Challenge for AI Chapter 8.3 Excerpt from Designing Ethical Algorithms Chapter 9 Gamification, Manipulation, and Data Analytics Summary of Readings Related Cases Notes Chapter 9.1 How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons Chapter 9.2 How Deepfakes Could Change Fashion Advertising Chapter 9.3 Excerpt from Ethics of Gamification Chapter 9.4 Excerpt from Manipulation, Privacy, and Choice Chapter 9.5 Excerpt from Ethics of the Attention Economy: The Problem of Social Media Addiction Chapter 10 Transparency and Accountability in Data Analytics Summary of Readings Related Cases Notes Chapter 10.1 Houston Teachers to Pursue Lawsuit over Secret Evaluation System Chapter 10.2 Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting back Chapter 10.3 When Algorithms Mess Up, the Nearest Human Gets the Blame Chapter 10.4 Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions Chapter 11 Ethics, AI, Research, and Corporations Summary of Readings Related Cases Notes Chapter 11.1 Google Research: Who Is Responsible for Ethics of AI? Chapter 11.2 The Scientist Qua Scientist Makes Value Judgments Chapter 11.3 Excerpt from Ethical Implications and Accountability of Algorithms Index "Floods are difficult to prevent but can be managed in order to reduce their environmental, social, cultural, and economic impacts. Flooding poses a serious threat to life and property, and therefore it's very important that flood risks be taken into account during any planning process. This handbook presents different aspects of flooding in the context of a changing climate and across various geographical locations. Written by experts from around the world, it examines flooding in various climates and landscapes, taking into account environmental, ecological, hydrological, and geomorphic factors, and considers urban, agriculture, rangeland, forest, coastal, and desert areas. Presents the main principles and applications of the science of floods, including engineering and technology, natural science, as well as sociological implications. Examines flooding in various climates and diverse landscapes, taking into account environmental, ecological, hydrological, and geomorphic factors. Considers floods in urban, agriculture, rangeland, forest, coastal, and desert areas. Covers flood control structures as well as preparedness and response methods. Written in a global context, by contributors from around the world"-- Provided by publisher Floods are difficult to prevent but can be managed in order to reduce their environmental, social, cultural, and economic impacts. Flooding poses a serious threat to life and property, and therefore it's very important that flood risks be taken into account during any planning process. This handbook presents different aspects of flooding in the context of a changing climate and across various geographical locations. Written by experts from around the world, it examines flooding in various climates and landscapes, taking into account environmental, ecological, hydrological, and geomorphic factors, and considers urban, agricultural, rangeland, forest, coastal, and desert areas.Features: Presents the main principles and applications of the science of floods, including engineering and technology, natural science, and sociological implications. Considers floods in urban, agricultural, rangeland, forest, coastal, and desert areas. Covers flood control structures as well as preparedness and response methods. Written in a global context, by contributors from around the world. "This textbook provides faculty the major concepts and cases to include in a class on the ethics of data analytics. The book is distinct as it focuses on ethics of data analytics, AI, and data (rather than infrastructure and reliability) and by explicitly linking data analytics to foundational business ethics theory"-- Provided by publisher