You will move from judging AI output to systematically stress-testing it. Run red-team bias protocols, detect hallucinations across many outputs, and assess whether images, audio, or video are synthetic, producing failure-mode reports, accuracy scorecards, and authenticity reports along the way. Verification builds the confidence needed to put AI-assisted work in front of stakeholders.

This module discusses the various risks and concerns associated with Generative AI, including business model risks, inaccuracies in AI-generated content, data security, and privacy concerns.

This module asks learners to deeply engage with the material by considering the presented ethical frameworks in the context of a hypothetical business dilemma and choosing an ethical course of action. Through deep framework understanding and use of tools for ethical decision-making, learners will be empowered to use ethical frameworks in their personal and professional lives thus protecting and improving the perception of their brand.

This module introduces learners to the foundational concepts of Responsible AI - exploring why ethical design, transparency, and accountability matter in modern AI systems. Learners will examine the core principles of Responsible AI, understand how bias and harm can emerge throughout the AI lifecycle, and discover how to embed ethical considerations into every stage of AI solution design.

In this project, you build a responsible AI governance package: a compact set of leadership artifacts that together set defensible AI practices for an organization. Working in ChatGPT, you will produce a bias-and-risk summary, a governance policy excerpt with accountability owners, and an ethics-and-mitigation section aligned to a recognized framework. It matters because a governance package is the evidence that you can lead responsible AI adoption in practice, not just describe it.