LEADER 04058nam 22005295 450 001 9910983301003321 005 20250512114728.0 010 $a9798868811661 024 7 $a10.1007/979-8-8688-1166-1 035 $a(CKB)37391272200041 035 $a(MiAaPQ)EBC31889585 035 $a(Au-PeEL)EBL31889585 035 $a(DE-He213)979-8-8688-1166-1 035 $a(OCoLC)1494976997 035 $a(CaSebORM)9798868811661 035 $a(OCoLC-P)1494976997 035 $a(EXLCZ)9937391272200041 100 $a20250124d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aResponsible AI in Practice $eA Practical Guide to Safe and Human AI /$fby Toju Duke, Paolo Giudici 205 $a1st ed. 2025. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2025. 215 $a1 online resource (171 pages) 300 $aIncludes index. 311 08$a9798868811654 327 $aPart I: Introduction -- Chapter 1: Responsible AI and AI Governance -- Part II: Technical risks (Internal to an organisation) -- Chapter 2. Accuracy -- Chapter 3. Robustness and Security -- Chapter 4: Explainability -- Part III: Ethical risks (External) -- Chapter 5. Fairness and Human Rights -- Chapter 6: Privacy -- Chapter 7: Sustainability -- Chapter 8: Human-Centered AI -- Part IV: Governance and Case studies -- Chapter 9: Governance Processes -- Chapters 10: Case Study. 330 $aThis book is the first practical book on AI risk assessment and management. It will enable you to evaluate and implement safe and accurate AI models and applications. The book features risk assessment frameworks, statistical metrics and code, a risk taxonomy curated from real-world case studies, and insights into AI regulation and policy, and is an essential tool for AI governance teams, AI auditors, AI ethicists, machine learning (ML) practitioners, Responsible AI practitioners, and computer science and data science students building safe and trustworthy AI systems across businesses, organizations, and universities. The centerpiece of this book is a risk management and assessment framework titled ?Safe Human-centered AI (SAFE-HAI),? which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance. Using several statistical metrics such as Area Under Curve (AUC), Rank Graduation Accuracy, and Shapley values, you will learn to apply Lorenz curves to measure risk and inequality across the different principles and will be equipped with a taxonomy/scoring rubric to identify and mitigate identified risks. This book is a true practical guide and covers a real-world case study using the proposed SAFE-HAI framework. The book will help you adopt standards and voluntary codes of conduct in compliance with AI risk and safety policies and regulations, including those from the NIST (National Institute of Standards and Technology) and EU AI Act (European Commission). What You Will Learn Know the key principles behind Responsible AI and associated risks Become familiar with risk assessment frameworks, statistical metrics, and mitigation measures for identified risks Be aware of the fundamentals of AI regulations and policies and how to adopt them Understand AI governance basics and implementation guidelines . 606 $aArtificial intelligence 606 $aArtificial intelligence$xMoral and ethical aspects 606 $aRisk assessment 615 0$aArtificial intelligence. 615 0$aArtificial intelligence$xMoral and ethical aspects. 615 0$aRisk assessment. 676 $a006.3 700 $aDuke$b Toju$01423935 701 $aGiudici$b Paolo$081878 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983301003321 996 $aResponsible AI in Practice$94317421 997 $aUNINA