Did you know that over 60% of organizations adopting AI struggle not with technology, but with aligning ethical practices and strategic goals across teams? Responsible AI success depends on more than just model performance—it depends on governance, purpose, and collaboration.

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Recommended experience
Skills you'll gain
- Data Governance
- Data Ethics
- Organizational Structure
- Strategic Leadership
- Technology Roadmaps
- Governance
- Change Management
- Business Management
- Ethical Standards And Conduct
- Cross-Functional Collaboration
- Artificial Intelligence and Machine Learning (AI/ML)
- Organizational Strategy
- Artificial Intelligence
- Decision Making
- Strategic Prioritization
- Enterprise Architecture
- Responsible AI
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There are 3 modules in this course
Learners master systematic frameworks for measuring and mitigating algorithmic bias using fairness metrics like demographic parity and equalized odds, enabling them to conduct enterprise-ready ethical risk assessments for AI deployment.
What's included
4 readings
Learners apply OKR frameworks and initiative mapping methodologies to evaluate AI roadmaps against business objectives, calculating ROI and identifying strategic gaps to secure executive support for AI investments.
What's included
4 readings
Learners develop comprehensive governance frameworks and organizational structures for AI Centers of Excellence, creating charters that standardize best practices and enable scalable, compliant AI operations across the enterprise.
What's included
3 readings
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