Debiasing AI

Rethinking the Intersection of Innovation and Sustainability
1. Auflage 2025
ISBN: 978-1-032-86978-0
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 300 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 594 g

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.

Zielgruppe

General, Professional Practice & Development, Professional Reference, and Undergraduate Advanced

Autoren/Hrsg.

Shin, Donghee

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Geisteswissenschaften Philosophie Ethik, Moralphilosophie

Inhaltsverzeichnis

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

Über Autor(innen)

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).