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Course Outline
Introduction to Responsible AI
- Principles of fairness, accountability, and transparency
- Regulatory drivers for responsible AI (EU AI Act, GDPR, etc.)
- The role of Ollama in enterprise AI governance
Bias Detection and Mitigation
- Identifying bias in model outputs
- Strategies for bias reduction and fairness improvement
- Evaluating model performance with fairness metrics
Safe Prompting and Alignment
- Prompt design for safety and reliability
- Mitigating risks of unsafe or harmful outputs
- Alignment techniques for enterprise applications
Content Filtering and Moderation
- Designing content filtering pipelines
- Implementing moderation safeguards
- Balancing user experience with compliance needs
Governance Workflows
- Defining governance frameworks for Ollama
- Workflow integration with compliance systems
- Model approval and audit procedures
Logging, Traceability, and Auditability
- Secure logging practices for AI systems
- Traceability of model decisions
- Audit readiness and reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments with responsible AI principles
- Lessons learned from real-world governance failures
- Building sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Understanding of AI/ML fundamentals
- Familiarity with compliance and governance concepts
- Experience with enterprise IT or model deployment environments
Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
14 Hours
