
Discover the five pillars of responsible ai—fairness, transparency, accountability, privacy, and safety and reliability—and learn to ask these questions to assess any ai system.
Discover how bias enters AI from data, design, and deployment, including unfair past decisions, proxies like zip code, and misaligned optimization, and learn where to fix it.
Analyze four real-world AI bias cases in justice, hiring, facial recognition, and healthcare. Defend a chosen fairness standard, scrutinize proxies, and ensure checks before deployment.
Define bias as a systematic, unfair outcome pattern and identify its sources—data, design choices, and deployment—then treat fairness as a values-based decision by asking who decides.
Assign a named human owner to every ai system, and clarify moral, legal, and organizational accountability to ensure redress when harm occurs.
Explore how governance closes the gap between ethics and practice by defining hard law, soft law, internal policy, and oversight for responsible AI.
Convert principles into programs by securing an executive sponsor, creating an ai inventory, applying risk tiering, and implementing a lightweight intake gate; governance matures from ad hoc to optimizing.
Shift left with responsible AI by design, embedding ethics across the lifecycle from design to retirement, with data sheets, model cards, and system cards guiding gate reviews and drift monitoring.
Explore how generative AI differs from traditional predictive AI, focusing on pattern completion, probability over truth, and its generality, scale, accessibility, guardrails, and governance.
Examine how AI relies on training data for copyright and IP, and who owns the resulting output.
Explore AI ethics and governance, uncover bias and fairness, demand transparency and accountability, and learn to vet generative tools and build a governance program.
Disclaimer: This course contains the use of artificial intelligence.
Artificial intelligence now decides who gets hired, who's approved for a loan, who gets flagged by a system, and what millions of people read each day — yet the professionals responsible for those systems are rarely the ones who built them. This course prepares you to use, buy, deploy, and oversee AI responsibly, whatever your role and whatever your technical background.
Written in plain language with no coding or advanced math, AI Ethics & Governance takes you from "what is AI, really?" to confidently leading a responsible-AI conversation in your own organization. Across eight modules and 31 short video lessons, you'll learn to recognize the major ethical risks of AI — bias, opacity, privacy harms, and misuse — and spot them in real systems. You'll apply the five pillars of responsible AI (fairness, transparency, accountability, privacy, and safety), navigate the frameworks and regulations that increasingly govern AI at work — including the EU AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001 — and grasp the distinct risks of generative AI, from hallucinations and deepfakes to copyright.
Every concept is grounded in well-documented, real-world cases and reinforced with hands-on, no-code labs, module quizzes, and a capstone project in which you produce a responsible-AI proposal for a real system. In roughly 8–10 hours, you'll gain the vocabulary, judgment, and practical tools to help your team adopt AI in a way that is not just powerful, but trustworthy.