AI Governance for Everyone

This program explores how Responsible AI and AI Governance help organizations build trustworthy, transparent, and accountable AI systems. You’ll begin by understanding the modern AI landscape, governance challenges, and the core principles of responsible AI. You’ll also explore how bias can emerge in AI systems, how AI decisions impact fairness and reliability, and the foundational concepts of AI governance, accountability, and governance risk mapping. You’ll then learn fairness, explainability, and AI risk management techniques used to evaluate and monitor machine learning systems. The program covers fairness metrics, human oversight, interpretability, transparency, and both local and global explanations. Through practical demonstrations using SHAP and LIME, you’ll analyze model predictions, interpret feature influence, and evaluate responsible AI behavior. Next, you’ll explore Responsible Generative AI and the governance challenges associated with foundation models and large language models (LLMs). You’ll examine risks such as hallucinations, misinformation, unsafe outputs, and reliability concerns, along with governance practices, safety evaluation techniques, and responsible deployment strategies for generative AI systems. Finally, you’ll examine AI governance frameworks, auditing principles, and global regulatory approaches used to manage AI risks at scale. You’ll learn about standards such as ISO 42001, AI auditing methodologies, governance risk assessment practices, and how organizations establish compliance, accountability, and effective AI oversight. By the end of this program, you will be able to: - Explain responsible AI principles, governance concepts, and modern AI governance challenges - Identify and evaluate bias, fairness risks, and human oversight requirements in AI systems - Interpret AI predictions using explainability techniques such as SHAP and LIME - Assess Generative AI and LLM risks, including hallucinations and unsafe outputs - Apply AI