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AI Governance & Reputation Risk for Boards: Policy Recommendations for a Responsible Future
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- FormatePub
- ISBN8231868612
- EAN9798231868612
- Date de parution12/07/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
AI Governance & Reputation Risk for Boards: Policy Recommendations for a Responsible Future offers a timely and actionable blueprint for corporate boards, regulators, and industry leaders navigating the accelerating AI revolution. Authored by Andy Bhatt, M. S.-M. I. S., this Think Tank report from SW & Associates confronts the widening gap between rapid AI adoption-especially Generative AI-and board-level readiness, exposing a growing "AI oversight debt" that poses ethical, legal, and reputational risks.
Drawing on cross-sector research and real-world case studies, the report highlights the most urgent challenges facing boards: algorithmic bias, data privacy breaches, black-box AI opacity, deepfakes, and the rise of deceptive "AI washing." It scrutinizes existing governance frameworks-including the OECD AI Principles, NIST AI RMF, and EU AI Act-and identifies critical shortcomings in corporate structures, such as fragmented AI ownership and low board literacy.
The report presents a structured and pragmatic set of policy recommendations: For boards: Build robust governance structures, mandate AI on the agenda, invest in AI fluency, and require explainability and ethical audits. For regulators: Support innovation-friendly regulation through risk-based frameworks, sandboxes, transparency mandates, and liability reform. For industry: Embed responsible AI from design to deployment through bias mitigation, human-in-the-loop systems, and strong data governance.
This comprehensive guide not only outlines the risks of inaction but positions responsible AI as a strategic differentiator. It urges organizations to treat ethics, transparency, and trust not as regulatory burdens, but as competitive advantages. As AI reshapes global industries, this report serves as both a warning and a roadmap for boards ready to lead responsibly in the age of intelligent systems.
Drawing on cross-sector research and real-world case studies, the report highlights the most urgent challenges facing boards: algorithmic bias, data privacy breaches, black-box AI opacity, deepfakes, and the rise of deceptive "AI washing." It scrutinizes existing governance frameworks-including the OECD AI Principles, NIST AI RMF, and EU AI Act-and identifies critical shortcomings in corporate structures, such as fragmented AI ownership and low board literacy.
The report presents a structured and pragmatic set of policy recommendations: For boards: Build robust governance structures, mandate AI on the agenda, invest in AI fluency, and require explainability and ethical audits. For regulators: Support innovation-friendly regulation through risk-based frameworks, sandboxes, transparency mandates, and liability reform. For industry: Embed responsible AI from design to deployment through bias mitigation, human-in-the-loop systems, and strong data governance.
This comprehensive guide not only outlines the risks of inaction but positions responsible AI as a strategic differentiator. It urges organizations to treat ethics, transparency, and trust not as regulatory burdens, but as competitive advantages. As AI reshapes global industries, this report serves as both a warning and a roadmap for boards ready to lead responsibly in the age of intelligent systems.
AI Governance & Reputation Risk for Boards: Policy Recommendations for a Responsible Future offers a timely and actionable blueprint for corporate boards, regulators, and industry leaders navigating the accelerating AI revolution. Authored by Andy Bhatt, M. S.-M. I. S., this Think Tank report from SW & Associates confronts the widening gap between rapid AI adoption-especially Generative AI-and board-level readiness, exposing a growing "AI oversight debt" that poses ethical, legal, and reputational risks.
Drawing on cross-sector research and real-world case studies, the report highlights the most urgent challenges facing boards: algorithmic bias, data privacy breaches, black-box AI opacity, deepfakes, and the rise of deceptive "AI washing." It scrutinizes existing governance frameworks-including the OECD AI Principles, NIST AI RMF, and EU AI Act-and identifies critical shortcomings in corporate structures, such as fragmented AI ownership and low board literacy.
The report presents a structured and pragmatic set of policy recommendations: For boards: Build robust governance structures, mandate AI on the agenda, invest in AI fluency, and require explainability and ethical audits. For regulators: Support innovation-friendly regulation through risk-based frameworks, sandboxes, transparency mandates, and liability reform. For industry: Embed responsible AI from design to deployment through bias mitigation, human-in-the-loop systems, and strong data governance.
This comprehensive guide not only outlines the risks of inaction but positions responsible AI as a strategic differentiator. It urges organizations to treat ethics, transparency, and trust not as regulatory burdens, but as competitive advantages. As AI reshapes global industries, this report serves as both a warning and a roadmap for boards ready to lead responsibly in the age of intelligent systems.
Drawing on cross-sector research and real-world case studies, the report highlights the most urgent challenges facing boards: algorithmic bias, data privacy breaches, black-box AI opacity, deepfakes, and the rise of deceptive "AI washing." It scrutinizes existing governance frameworks-including the OECD AI Principles, NIST AI RMF, and EU AI Act-and identifies critical shortcomings in corporate structures, such as fragmented AI ownership and low board literacy.
The report presents a structured and pragmatic set of policy recommendations: For boards: Build robust governance structures, mandate AI on the agenda, invest in AI fluency, and require explainability and ethical audits. For regulators: Support innovation-friendly regulation through risk-based frameworks, sandboxes, transparency mandates, and liability reform. For industry: Embed responsible AI from design to deployment through bias mitigation, human-in-the-loop systems, and strong data governance.
This comprehensive guide not only outlines the risks of inaction but positions responsible AI as a strategic differentiator. It urges organizations to treat ethics, transparency, and trust not as regulatory burdens, but as competitive advantages. As AI reshapes global industries, this report serves as both a warning and a roadmap for boards ready to lead responsibly in the age of intelligent systems.
