Debiasing AI Rethinking the Intersection of Innovation and Sustainability
Produktdetaljer
Biografisk notat
Donghee “Don” Shin is a Professor at Texas Tech University, USA. His work contributes to the role of online algorithmic intermediaries in shaping people’s online consumption. He has published widely in both communication and information systems. He served as the Principal Investigator of a large-scale national research project. He was awarded an Endowed Chair Professorship by the Ministry of Education in Korea as well as a Samsung Endowed Chair. He also served as Regent Professor at Sungkyunkwan University from 2009 to 2016. Shin was inducted as a Fellow of the International Communication Association (ICA Fellow).
<p>"In the swiftly evolving world of AI, technology now influences nearly every corner of human life. <i>Debiasing AI </i>explores the profound questions that arise when machines gain the power to make decisions impacting society. It examines not only the ethical principles that should guide AI development, but also pays attention to phenomenological and epistemological aspects. As we stand at the threshold of a future where AI will shape human lives in unpredictable ways, <i>Debiasing AI </i>is an invitation to consider how we can build a more responsible, just, and equitable world through mindful technology."</p><p><b>Mark Coekelbergh,</b> <i>University of Vienna</i></p><p>“<i>Debiasing AI</i> reviews and explores critical issues in how AI technology can detract from or contribute to a more just, humane, and equitable world. Topics range from the extent to which AI can be moral or ethical, how algorithms can nudge users toward more or less biased decisions, and how algorithms may be proactively designed to inoculate against misinformation.”</p><p><b>Ronald E. Rice,</b> <i>University of California, Santa Barbara</i></p><p>“<i>Debiasing AI</i> is an insightful exploration of the ethical challenges posed by AI. Don Shin masterfully navigates complex topics like fairness, transparency, and accountability, offering readers an essential resource in understanding AI’s moral dimensions. This is an indispensable book for anyone looking to grasp the ethical landscape of AI."</p><p><b>Karamjit S. Gill,</b> <i>Editor-in-Chief, AI and Society</i></p><p>“<i>Debiasing AI</i> is a groundbreaking contribution to AI ethics, providing a thoughtful and scholarly exploration of the pressing questions that define our technological era. Dr. Shin combines academic rigor with real-world examples, making this work an indispensable read for researchers, students, and practitioners dedicated to advancing the field of AI ethics."</p><p><b>Mohammed Ibahrine,</b> <i>Northwestern University</i></p>
In an era where artificial intelligence (AI) drives unprecedented change, Debiasing AI examines the vital intersection of technology, innovation, and sustainability. This book confronts the pressing challenge of bias in AI systems, exploring its far-reaching implications for fairness, trust, and ethical practices. Through a multidisciplinary lens, the author examines how human biases are embedded in large language models, amplified by coded machine learning, and propagated through trained algorithms. Practical strategies are offered to address these issues, paving the way for the development of more equitable and inclusive AI technologies.
With actionable insights, empirical case studies, and theoretical frameworks, Debiasing AI offers a roadmap for designing AI technologies that are not only innovative but also ethically sound and equitable. A must-read for scholars, industry leaders, and policymakers, this book inspires a reimagining of AI’s role in creating a fairer and more sustainable future.
Debiasing AI examines the vital intersection of technology, innovation, and sustainability. It addresses the pressing challenge of bias in AI systems, exploring its far-reaching implications for fairness, trust, and ethical practices. A must-read for scholars, industry leaders, and policymakers.
Introduction: Debiasing AI: Rethinking the Intersection of Innovation and Sustainability
PART ONE Ontology of AI Ethics: Ethical AI Principles
1 AI and Moral Agency: Can AI Have a Sense of Morality?
2 Decoding Algorithmic Privacy: How to Address Privacy Issues Raised by AI
3 AI and Transparency: In Transparency We Trust
PART TWO Phenomenology of AI Ethics: How People Experience AI Ethics
4 Algorithmic Bias and Trust: How to Debias and Build Trust in AI
5 Algorithmic Nudge: A Nudge to Counter Algorithmic Bias
6 Algorithmic Heuristics: How People Evaluate the Ethics of Deepfakes
PART THREE Epistemology of AI Ethics: Mechanism of Understanding AI Ethics
7 Algorithmic Equity: How Humans Understand AI Morality
8 The Ethics of AI Acceptance: How Ethical Heuristics Drive AI Adoption
9 Responsible AI and the Newsroom: How Does AI Journalism Make Sense of AI Ethics?
PART FOUR Governance of AI Ethics: Striking the Right Balance Ethics and Regulation
10 The Moral Code: The Intersection of Ethics and Regulation in AI
11 Diversity-Aware AI: Designing AI Systems That Reflect Humanity
12 Algorithmic Inoculation: Immunizing Minds Against Bias
Produktdetaljer
Biografisk notat
Donghee “Don” Shin is a Professor at Texas Tech University, USA. His work contributes to the role of online algorithmic intermediaries in shaping people’s online consumption. He has published widely in both communication and information systems. He served as the Principal Investigator of a large-scale national research project. He was awarded an Endowed Chair Professorship by the Ministry of Education in Korea as well as a Samsung Endowed Chair. He also served as Regent Professor at Sungkyunkwan University from 2009 to 2016. Shin was inducted as a Fellow of the International Communication Association (ICA Fellow).