AI Ethics & Governance
Navigate the complex landscape of AI ethics and governance. This path covers bias detection, fairness metrics, regulatory compliance, transparency requirements, and how to build governance frameworks for responsible AI deployment.
What You'll Learn
- Understand key AI ethics principles and their practical implications
- Detect and mitigate bias in AI systems
- Navigate AI regulations including the EU AI Act
- Build an AI governance framework for your organization
- Implement transparency and explainability requirements
- Conduct AI impact assessments and risk evaluations
- Establish responsible AI development practices
Course Lessons
Foundations of AI Ethics
18 min readExplore core ethical principles for AI — fairness, accountability, transparency, and privacy — and how they translate into practical requirements.
Read lesson →Understanding and Detecting AI Bias
20 min readLearn how bias enters AI systems through data, design, and deployment, and explore tools and techniques for detecting and measuring bias.
Read lesson →AI Regulation: The Global Landscape
18 min readNavigate the EU AI Act, US AI executive orders, and emerging regulations worldwide. Understand compliance requirements and their timeline.
Building an AI Governance Framework
20 min readDesign a practical governance framework for your organization covering risk assessment, approval workflows, monitoring, and accountability structures.
Transparency and Explainability in Practice
18 min readImplement transparency requirements including model documentation, decision explanations, and user-facing AI disclosures.
AI Impact Assessments
15 min readConduct thorough impact assessments before deploying AI systems, covering risks to individuals, groups, and society.
Responsible AI Development Practices
18 min readEstablish development practices that embed ethics throughout the AI lifecycle, from data collection to deployment and monitoring.
Read lesson →Related Learning Paths
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