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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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 digital media. 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 and served as Regent Professor at Sungkyunkwan U from 2011 to 2016. Shin was inducted as a Fellow of the International Communication Association (ICA).
Inhaltsangabe
Part 1: Ontology of AI Ethics: Ethical AI Principles 1. AI and Moral Agency: Can AI Have a Sense of Morality? 2. AI and Privacy: How to Address Privacy Issues Raised by AI 3. AI and Transparency: In Transparency We Trust Part 2: 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 3: Epistemology of AI Ethics: Mechanism of Understanding AI Ethics 7. Algorithmic Equity: How Humans Understand AI Morality 8. The Role of Ethics in AI Acceptance: How Ethical Heuristics Drive AI Adoption 9. Responsible AI in Journalism: How Does AI Journalism Make Sense of AI Ethics? Part 4: Governance of AI Ethics: Striking the Right Balance Ethics and Regulation 10. AI Governance: The Intersection of Ethics and Regulation in AI 11. Diversity-Aware AI: Designing AI Systems that Reflect Humanity 12. Algorithmic Inoculation: Building Cognitive Immunity Against Bias
Part 1: Ontology of AI Ethics: Ethical AI Principles 1. AI and Moral Agency: Can AI Have a Sense of Morality? 2. AI and Privacy: How to Address Privacy Issues Raised by AI 3. AI and Transparency: In Transparency We Trust Part 2: 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 3: Epistemology of AI Ethics: Mechanism of Understanding AI Ethics 7. Algorithmic Equity: How Humans Understand AI Morality 8. The Role of Ethics in AI Acceptance: How Ethical Heuristics Drive AI Adoption 9. Responsible AI in Journalism: How Does AI Journalism Make Sense of AI Ethics? Part 4: Governance of AI Ethics: Striking the Right Balance Ethics and Regulation 10. AI Governance: The Intersection of Ethics and Regulation in AI 11. Diversity-Aware AI: Designing AI Systems that Reflect Humanity 12. Algorithmic Inoculation: Building Cognitive Immunity Against Bias
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