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AI Governance & Responsible AI: From Reactive Compliance to Embedded Practice
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AI Governance & Responsible AI
Ensuring Ethical, Fair, and Accountable AI Systems
Frameworks for responsible development and deployment of AI. Includes AI policy, model governance, responsible AI principles, fairness assessment, bias detection and mitigation, explainability, transparency, ethical oversight, and regulatory compliance. Maturity progresses from reactive risk management to proactive, embedded responsible AI practices across the AI lifecycle.
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Banks Have Already Solved Your AI Governance Problem
Explores how model risk management frameworks from regulated industries can be adapted for AI systems across sectors. Covers model validation approaches, ongoing monitoring, documentation standards, three lines of defense, and governance processes that balance innovation velocity with appropriate oversight.
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Why We Keep Demanding Explainable AI in All the Wrong Places
Examines the tension between model explainability and performance, providing frameworks for navigating this tradeoff based on risk, regulatory requirements, and stakeholder needs. Covers post-hoc explanation techniques, inherently interpretable models, and when transparency truly matters vs. when it's performative.
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