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.
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.
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.
Inhaltsangabe
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.
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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