Overview

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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 governance, auditing, and risk management practices using global frameworks and standards This program is designed for AI practitioners, machine learning engineers, data scientists, governance professionals, compliance teams, technology leaders, and analysts who want to build, evaluate, and govern trustworthy AI systems. A foundational understanding of machine learning concepts and Python will help maximize your learning experience. Join us to explore Responsible AI, fairness, explainability, governance, and AI risk management practices that help create transparent, trustworthy, and accountable intelligent systems.

Syllabus

Foundations of Responsible AI & Governance

Build a foundation in responsible AI and governance by understanding AI risks, ethical challenges, and governance principles in modern AI systems. Explore how organizations manage accountability, trust, and AI risks through practical bias analysis and governance exercises.

Fairness, Explainability & AI Risk Management

Explore fairness, explainability, and AI risk management by understanding bias, fairness trade-offs, human oversight, and AI decision behavior. Apply local and global explanation techniques through practical fairness and explainability exercises.

Responsible Generative AI, Regulation & AI Auditing

Build an understanding of responsible generative AI, governance frameworks, and AI auditing practices. Explore foundation model risks, hallucinations, unsafe AI outputs, and perform hands-on AI risk assessment and governance analysis exercises.

Course Wrap-Up and Assessment

This final module focuses on evaluating responsible AI practices and their real-world application. You will demonstrate your ability to analyze AI risks, assess fairness and explainability, evaluate generative AI challenges, and apply governance and auditing concepts across different AI systems. You will also perform governance risk assessments and responsible AI evaluations using structured analysis techniques. By the end, you will be able to assess and communicate trustworthy, fair, transparent, and responsible AI practices.