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Full Description
How can we build and govern trustworthy AI? Operationalizing Responsible AI brings together leading scholars and practitioners to address this urgent question. Each chapter explores a key dimension of responsibility - fairness, explainability, psychological safety, accountability, consent, transparency, auditability, and contextualization - defining what it means, why it matters, and how it can be achieved in practice. Through interdisciplinary perspectives and real-world examples, the book bridges ethical principles, legal frameworks such as the EU AI Act, and technical approaches including explainable AI and audit methodologies. Written for researchers, policymakers, and professionals, the book offers both conceptual clarity and practical guidance for advancing Responsible AI that is fair, transparent, and aligned with human values.
Contents
Preface; 1. Can AI be Fair? Yi-Chieh Lee, Jingshu Li, Renwen Zhang and Nan Liu; 2. Can AI be Explainable? Simone Stumpf; 3. Can AI be Psychologically Safe? Uthman Ali; 4. Can AI be Accountable? Andrew L. Kun; 5. Can AI be Consentful? Giada Pistilli and Bruna Selin Trevelin; 6. Can AI be Transparent? Fabian Linde, Dalia Ali and Orestis Papakyriakopoulos; 7. Can AI be Auditable? Himanshu Verma, Kirtan Shyamal Padh and Eva Thelisson; 8. Can AI be Contextualized? Glen Berman and James Smithies; Index.



