Course Overview
Responsible AI governance demands more than technical expertise; it requires ethical leadership that protects people, builds public trust, and delivers clear value to communities. For leaders overseeing AI initiatives, the challenge is ensuring systems operate fairly, transparently, and accountably, because bias, privacy gaps, and lack of explainability can quickly become public trust crises. This course will equip you to lead ethical AI assessments, apply structured risk management approaches, and build governance systems that maintain public confidence.
In this course, you will develop practical skills for conducting ethical risk assessments and making responsible AI governance decisions. You'll examine how to apply the NIST (National Institute of Standards and Technology) AI Risk Management Framework to identify and mitigate risks while exploring how AI can serve as both a bias detector and bias prevention tool. Through hands-on activities and projects, you'll navigate ethical dilemmas involving bias, fairness, and accountability in AI systems.
You will also explore how ethical leadership principles translate into operational controls and conduct workplace assessments that surface hidden bias before it affects communities. Working with an AI initiative, you'll use professional ethics assessment tools and governance structures that demonstrate public accountability. By the end of this course, you'll be prepared to lead ethical AI initiatives, apply structured risk management frameworks, and develop governance recommendations that protect people while delivering public value.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Building AI Foundations for Public Leaders
- Powering AI With Data
Key Course Takeaways
- Apply ethical leadership principles to AI initiatives and identify where accountability must remain visible to maintain public trust
- Apply the NIST AI Risk Management Framework to assess organizational AI risks and design structured governance controls
- Identify bias patterns in AI systems and develop mitigation strategies that address both technical and institutional sources of unfairness
- Evaluate AI initiatives using ethical leadership frameworks and create accountability plans that demonstrate visible leadership responsibility
- Detect multiple forms of bias, design mitigation controls, and recommend institutional changes to sustain fairness in AI systems
How It Works
Who Should Enroll
- Government executives and agency leaders responsible for AI strategy, digital transformation, technology, innovation, or organizational leadership
- Program and operational leaders managing AI initiatives, modernization efforts, or public service delivery
- Data, privacy, cybersecurity, risk, and compliance professionals responsible for data governance, ethical AI, security, ethics, or regulatory compliance
- Public policy professionals, legislators, and legislative staff shaping AI policy, regulation, oversight, or public accountability
- Procurement, acquisition, and contracting professionals responsible for evaluating, purchasing, or managing AI technologies and vendor relationships
- Consultants, GovTech providers, nonprofits, and other organizations serving the public sector that advise, implement, or support responsible AI adoption
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