This course contains the use of artificial intelligence.

The 3 Week Responsible AI & Governance Certification is designed to help learners understand how to build, evaluate, manage, and govern AI systems in a way that is ethical, transparent, safe, and aligned with real-world business expectations. As organizations adopt artificial intelligence, generative AI, automation, and decision-support systems at a faster pace, the need for Responsible AI, AI governance, risk management, and compliance has become more important than ever.

This course begins with the foundations of AI ethics, exploring why responsible AI matters today across business, legal, and societal contexts. You will examine real-world AI failures and understand how poor design, weak oversight, biased data, and unclear accountability can lead to serious consequences. You will learn the major types of AI bias, including data bias, model bias, and human bias, and see where bias can enter the AI lifecycle from data collection to deployment.

The course then introduces the core ideas behind fairness in AI, including conceptual fairness metrics, tradeoffs, and why fairness cannot be treated as a single universal rule. You will also explore major AI risks such as hallucinations, misuse, unreliable outputs, safety failures, and harmful downstream impacts. Through the Week 1 lab, you will conduct an AI Risk & Bias Assessment to identify risks in an AI system and think critically about mitigation strategies.

In Week 2, the course moves into AI governance frameworks, regulations, and organizational accountability. You will learn what governance means in the context of AI and how roles, responsibilities, policies, workflows, and controls help organizations manage AI responsibly. The course introduces global regulatory trends, including the EU AI Act, the evolving US AI landscape, and the growing need for AI oversight. You will study the NIST AI Risk Management Framework, including the practical ideas behind map, measure, and manage. You will also learn how risk-based classification works under the EU AI Act, including the difference between high-risk and low-risk AI systems. The Week 2 lab guides you through designing a practical governance framework for an AI system.

In Week 3, you will focus on implementation, monitoring, audits, and long-term responsible AI operations. You will learn how to build responsible AI principles into systems through guardrails, constraints, design-time governance, and runtime governance. You will explore model monitoring, incident response, drift detection, internal audits, documentation, traceability, and vendor risk management. The course also shows how strong AI governance can become a strategic advantage by building trust, improving reliability, reducing risk, and strengthening organizational credibility.

By the end of this certification, you will have a practical understanding of Responsible AI, AI ethics, AI governance, NIST AI RMF, EU AI Act, auditing, risk management, and compliance workflows for modern AI systems.