Overview

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This course gives you the ethics, verification, and governance skills to use AI responsibly at work. You will apply responsible AI principles, audit systems for bias, red-team and verify AI outputs, assess organizational risk, work through real ethical dilemmas, and draft a governance policy tailored to your organization. Every activity produces a defensible artifact: a bias audit, a risk register, an ethics analysis, or a policy document. By the end, you will be able to set and defend responsible AI practices for your team. Designed for leaders accountable for how AI is used in their teams and organizations, including risk, compliance, and people implications. No coding required. You will work with ChatGPT, the Responsible AI principles model, and frameworks like the NIST AI RMF. By the end, you will audit AI systems for fairness and bias, verify outputs through red-teaming and hallucination detection, assess organizational AI risk, and draft governance policy and evaluate frameworks for fit.

Syllabus

AI Ethics and Bias Foundations
- You’ll explore practical tools and frameworks to build ethical and trustworthy AI systems. Through hands-on experience with tools like AI Fairness 360, you’ll learn to audit models for bias, enhance explainability, and integrate transparency and compliance into your workflow. You’ll also develop and apply responsible AI checklists, ensuring your AI projects align with ethical and regulatory standards without compromising innovation. You will explore how to identify and mitigate ethical risks, detect and document bias, and evaluate model performance beyond accuracy metrics.
AI Output Verification and Red-Teaming
- 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.
Navigating AI Risks in Your Organization
- 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.
Ethical AI in Practice: Case Studies and Dilemmas
- 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.
Building AI Governance Policy for Your Organization
- 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.
Project Module: Build a Responsible AI Governance Package
- 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.

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Professionals from the Industry

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