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024 7 _a10.1007/979-8-8688-0983-5
_2doi
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100 1 _aSayles, James.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989806
245 1 0 _aPrinciples of AI Governance and Model Risk Management
_h[electronic resource] :
_bMaster the Techniques for Ethical and Transparent AI Systems /
_cby James Sayles.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXXXI, 472 p. 16 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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505 0 _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.
520 _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.
650 0 _aRisk management.
_959047
650 0 _aFinancial risk management.
_959046
650 0 _aBusiness.
_958353
650 0 _aManagement science.
_957941
650 0 _aArtificial intelligence.
_958441
650 0 _aMachine learning.
_955059
650 1 4 _aIT Risk Management.
_969859
650 2 4 _aRisk Management.
_959047
650 2 4 _aBusiness and Management.
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aMachine Learning.
_955059
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868809828
776 0 8 _iPrinted edition:
_z9798868809842
776 0 8 _iPrinted edition:
_z9798868813566
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-0983-5
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