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Introduction

1 Value-Laden Biases in Data Analytics
Summary of Readings
Related Cases
Notes
1.1 This Is the Stanford Vaccine Algorithm That Left out Frontline Doctors
1.2 Racial Bias in a Medical Algorithm Favors White Patients over Sicker Black Patients
1.3 Excerpt from Do Artifacts Have Politics?
1.4 Excerpt from Bias in Computer Systems
1.5 Excerpt from Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning
1.6 Algorithmic Bias and Corporate Responsibility: How Companies Hide behind the False Veil of the Technological Imperative

2 Ethical Theories and Data Analytics
Summary of Readings
Virtue Ethics
Critical Approaches, Ethics, and Power
Related Cases
Notes
2.1 Language Models Like GPT-3 Could Herald a New Type of Search Engine
2.2 How to Make a Chatbot That Isn’t Racist or Sexist
2.3 This Facial Recognition Website Can Turn Anyone into a Cop?or a Stalker
2.4 Excerpt from Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting
2.5 Ethics of Care as Moral Grounding for AI
2.6 Excerpt from Operationalizing Critical Race Theory in the Marketplace

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
3.1 Finding Consumers, No Matter Where They Hide: Ad Targeting and Location Data
3.2 How a Company You’ve Never Heard of Sends You Letters about Your Medical Condition
3.3 Excerpt from A Contextual Approach to Privacy Online
3.4 Excerpt from Understanding Privacy Online: Development of a Social Contract Approach to Privacy
3.5 Privacy Law for Business Decision-Makers in the United States
3.6 Wrongfully Accused by an Algorithm
3.7 Facial Recognition Is Accurate, If You’re a White Guy
3.8 Excerpt from Datasheets for Datasets

4 Surveillance and Power
Summary of Readings
Related Cases
Notes
4.1 Twelve Million Phones, One Dataset, Zero Privacy
4.2 The Secretive Company That Might End Privacy as We Know It
4.3 Excerpt from Big Brother to Electronic Panopticon
4.4 Excerpt from Privacy, Visibility, Transparency, and Exposure

5 The Purpose of the Corporation and Data Analytics
Summary of Readings
Related Cases
Notes
5.1 The Quiet Growth of Race-Detection Software Sparks Concerns over Bias
5.2 A Face-Scanning Algorithm Increasingly Decides Whether You Deserve the Job
5.3 Excerpt from Managing for Stakeholders
5.4 Excerpt from The Problem of Corporate Purpose
5.5 Recommending an Insurrection: Facebook and Recommendation Algorithms
5.6 Excerpt from Can Socially Responsible Firms Survive in a Competitive Environment?

6 Fairness and Justice in Data Analytics
Summary of Readings
Related Cases
Notes
6.1 Machine Bias
6.2 Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say
6.3 Major Universities Are Using Race as a “High Impact Predictor” of Student Success
6.4 Excerpt from Distributive Justice
6.5 Excerpt from Justice as Fairness
6.6 Excerpt from Tyranny and Complex Equality

7 Discrimination and Data Analytics
Summary of Readings
Related Cases
Notes
7.1 Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women
7.2 Bias Isn’t the Only Problem with Credit Scores?and No, AI Can’t Help
7.3 Excerpt from Big Data’s Disparate Impact
7.4 Excerpt from Where Fairness Fails: Data, Algorithms, and the Limits of Antidiscrimination Discourse

8 Creating Outcomes and Accuracy in Data Analytics
Summary of Readings
Related Cases
Notes
8.1 Pasco’s Sheriff Uses Grades and Abuse Histories to Label Schoolchildren Potential Criminals: : The Kids and Their Parents Don’t Know
8.2 Excerpt from Reliance on Metrics is a Fundamental Challenge for AI
8.3 Excerpt from Designing Ethical Algorithms

9 Gamification, Manipulation, and Data Analytics
Summary of Readings
Related Cases
Notes
9.1 How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons
9.2 How Deepfakes Could Change Fashion Advertising
9.3 Excerpt from Ethics of Gamification
9.4 Excerpt from Manipulation, Privacy, and Choice
9.5 Excerpt from Ethics of the Attention Economy: The Problem of Social Media Addiction

10 Transparency and Accountability in Data Analytics
Summary of Readings
Related Cases
Notes
10.1 Houston Teachers to Pursue Lawsuit over Secret Evaluation System
10.2 Cheating-Detection Companies Made Millions During the Pandemic. Now Students Are Fighting back
10.3 When Algorithms Mess Up, the Nearest Human Gets the Blame
10.4 Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions

11 Ethics, AI, Research, and Corporations
Summary of Readings
Related Cases
Notes
11.1 Google Research: Who Is Responsible for Ethics of AI?
11.2 The Scientist Qua Scientist Makes Value Judgments
11.3 Excerpt from Ethical Implications and Accountability of Algorithms

Index

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Ethics of data and analytics : concepts and cases 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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0002926545 006.312 -A22-8 서울관 서고(열람신청 후 1층 대출대) 이용가능

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알라딘제공

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.



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.



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