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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory factors driving responsible AI adoption (e.g., EU AI Act, GDPR)
- Ollama's function in enterprise AI governance
Identifying and Addressing Bias
- Recognizing bias within model outputs
- Techniques for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Designing prompts for safety and reliability
- Mitigating risks associated with unsafe or harmful outputs
- Applying alignment techniques to enterprise applications
Content Filtering and Moderation
- Constructing content filtering pipelines
- Deploying moderation safeguards
- Striking a balance between user experience and compliance obligations
Governance Workflows
- Formulating governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Auditing
- Best practices for secure logging in AI systems
- Tracking the traceability of model decisions
- Preparing for audits and establishing reporting mechanisms
Case Studies and Industry Best Practices
- Enterprise implementations adhering to responsible AI principles
- Insights derived from real-world governance challenges
- Cultivating sustainable and ethical AI practices
Conclusion and Future Directions
Requirements
- A solid grasp of AI/ML fundamentals
- Knowledge of compliance and governance concepts
- Practical experience with enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
