In an era where artificial intelligence drives unprecedented change, Debiasing AI examines the vital intersection of technology, innovation, and sustainability. The book addresses 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.
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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