05075nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001500162072001700177072002300194072001400217072001600231100007600247245016500323250001800488264005400506300004500560336002600605337002600631338003600657347002400693505149100717520186902208650002104077650003104098650001404129650002404143650002904167650002204196650002404218650002104242650002904263650002904292650002204321710003404343773002604377776003604403776003604439776003604475856004604511979-8-8688-0983-5DE-He21320251030114648.0cr nn 008mamaa241228s2024    xxu|    s    |||| 0|eng d  a97988688098357 a10.1007/979-8-8688-0983-52doi 4aHD61 7aUR2bicssc 7aGPQD2bicssc 7aCOM0530002bisacsh 7aUR2thema 7aGPQD2thema1 aSayles, James.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aPrinciples of AI Governance and Model Risk Managementh[electronic resource] :bMaster the Techniques for Ethical and Transparent AI Systems /cby James Sayles.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXXXI, 472 p. 16 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 1 - The Current State of AI Governance and Model Risk Management -- Chapter 2 - AI Strategy and AI Governance Interoperability -- Chapter 3 - How to Sound Like an AI Governance Guru -- Chapter 4 - Designing a Well-Governed AI Lifecycle Model -- Chapter 5 - AI Governance for Trustworthy AI – The Governances in AI Governance -- Chapter 6 - Designing Your AI Governance Framework -- Chapter 7 - AI Governance and Oversight Model -- Chapter 8 - Managing and Addressing AI Compliance -- Chapter 9 - Integrating AI Governance with Enterprise Governance Risk and Compliance -- Chapter 10 - AI Policy Management and Enforcement -- Chapter 11 - Maintaining Privacy within your AI Governance Model -- Chapter 12 - Human Oversight of AI Systems -- Chapter 13 - The Power of Stakeholder Engagement in AI Governance -- Chapter 14 - Considering the Environmental Impacts of AI Systems -- Chapter 15 - Developing the Protocols for Rapid Response in Case of an AI-Related Crisis -- Chapter 16 - Capacity Building for AI Actors -- Chapter 17 - Intellectual Property Rights with AI Technologies -- Chapter 18 - Auditing AI Systems -- Chapter 19 - AI Model Inventory and Facts -- Chapter 20 - Ramesh -- Chapter 21 – AI Governance and SDLC Integration -- Chapter 22: AI Through the Lens of Non-Technical Business Leaders: Embracing AI with Caution -- Chapter 23: Navigating the AI Frontier with this Sales Bible: Sales and Marketing Strategies for AI Governance and Risk Management Solutions.  aNavigate the complex landscape of Artificial Intelligence (AI) governance and model risk management using a holistic approach encompassing people, processes, and technology. This book provides practical guidance, oversight structure and centers of excellence, and actionable insights for organizations seeking to harness the power of AI responsibly, ethically, and transparently. By addressing the technical, ethical, and societal dimensions of AI governance, organizations will be empowered to build trustworthy AI systems that benefit both their bottom line and the broader community. Featuring successful mitigating controls based on proven use cases, the book underscores the importance of aligning AI strategy with AI governance, striking a balance between AI innovation, risk mitigation as well as broader business goals. You’ll receive pointers for designing a well-governed AI development lifecycle, emphasizing transparency, accountability, and continuous monitoring throughout the AI development lifecycle. This book highlights the importance of collaboration between stakeholders, i.e., boards of directors, CxOs, corporate counsel, compliance officers, audit executives, data scientists, developers, validators, etc. You’ll gain practical advice on addressing the challenges related to the ownership of AI-generated content and models, stressing the need for legal frameworks and international collaboration. You’ll also learn the importance of auditing AI systems, developing protocols for rapid response in case of AI-related crises, and building capacity for AI actors through education. Principles of AI Governance and Model Risk Management demonstrates its value-added uniqueness by detailing a strategy to ensure a cohesive approach to managing AI-related risks, global compliance, policy, privacy, and AI-human collaboration and oversight. 0aRisk management. 0aFinancial risk management. 0aBusiness. 0aManagement science. 0aArtificial intelligence. 0aMachine learning.14aIT Risk Management.24aRisk Management.24aBusiness and Management.24aArtificial Intelligence.24aMachine Learning.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886880982808iPrinted edition:z979886880984208iPrinted edition:z979886881356640uhttps://doi.org/10.1007/979-8-8688-0983-5