Ethics of data and analytics : concepts and cases
著者
書誌事項
Ethics of data and analytics : concepts and cases
（An Auerbach book）
CRC Press, 2022
- : hbk
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注記
Includes bibliographical references and index
収録内容
- Value-laden biases in data analytics
- Ethical theories and data analytics
- Privacy, data, and shared responsibility
- Surveillance and power
- The purpose of the corporation and data analytics
- Fairness and justice in data analytics
- Discrimination and data analytics
- Creating outcomes and accuracy in data analytic
- Gamification, manipulation, and data analytics
- Transparency and accountability in data analytics
- Ethics, AI, research, and corporations
内容説明・目次
内容説明
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.
目次
Introduction. 1 Value-Laden Biases in Data Analytics. 2 Ethical Theories and Data Analytics. 3 Privacy, Data, and Shared Responsibility. 4 Surveillance and Power. 5 The Purpose of the Corporation and Data Analytics. 6 Fairness and Justice in Data Analytics. 7 Discrimination and Data Analytics. 8 Creating Outcomes and Accuracy in Data Analytics. 9 Gamification, Manipulation, and Data Analytics. 10 Transparency and Accountability in Data Analytics. 11 Ethics, AI, Research, and Corporations. Index.
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