Articoli correlati a Operationalizing Responsible AI
Sinossi
A comprehensive guide exploring eight foundations of Responsible AI, from fairness and transparency to auditability and contextualization.
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Informazioni sugli autori
Daniele Quercia is Director of Responsible AI at Nokia Bell Labs Cambridge, United Kingdom and Professor at Politecnico di Torino, Italy. Named one of Fortune's Data All-Stars, he has spoken at TED and published widely on urban computing and Responsible AI after research roles at Yahoo Labs, Cambridge, and MIT.
Marios Constantinides is a Senior Research Scientist at CYENS Centre of Excellence, Cyprus and Honorary Lecturer at University College London, United Kingdom. His research on Human-Computer Interaction and Responsible AI has received international recognition, with publications in top venues and press coverage.
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Operationalizing Responsible AI (Paperback or Softback)
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Paperback. Condizione: New. 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. Codice articolo LU-9781009625197
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Paperback. Condizione: new. Paperback. 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. This interdisciplinary volume brings together global experts to explore what it truly means for AI to be responsible. Covering fairness, explainability, safety, accountability, consent, transparency, auditability, and context, it provides practical insights for scholars, practitioners, and policymakers designing trustworthy AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781009625197
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Operationalizing Responsible AI
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Paperback. Condizione: New. 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. Codice articolo LU-9781009625197
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Operationalizing Responsible Ai
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Operationalizing Responsible Ai
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