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[null,"1.","Record Nr.",null,"UNINA9910919826103321"]
[null,null,"Autore",null,"Sayles James"]
[null,null,"Titolo",null,"Principles of AI Governance and Model Risk Management : Master the Techniques for Ethical and Transparent AI Systems / / by James Sayles"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024"]
[null,null,"ISBN",null,"9798868809835"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (491 pages)"]
[null,null,"Disciplina",null,"658.155"]
[null,null,"Soggetti",null,"Artificial intelligence - Management"]
[{"$nulls":4},"Artificial intelligence - Moral and ethical aspects"]
[{"$nulls":4},"Artificial intelligence - Risk management"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Chapter 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."]
[null,null,null,"Sommario/riassunto","Navigate the complex landscape of Artificial Intelligence (AI) "]
[{"$nulls":4},"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."]