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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory drivers for responsible AI (e.g., EU AI Act, GDPR)
- The role of Ollama in enterprise AI governance
Bias Detection and Mitigation
- Identifying bias in model outputs
- Strategies for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Prompt engineering for safety and reliability
- Reducing the risk of unsafe or harmful outputs
- Alignment techniques for enterprise applications
Content Filtering and Moderation
- Designing pipelines for content filtering
- Implementing moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Defining governance frameworks for Ollama
- Integrating workflows with compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Auditability
- Secure logging practices for AI systems
- Tracing the lineage of model decisions
- Audit readiness and reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments adhering to responsible AI principles
- Insights from real-world governance failures
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Knowledge of AI/ML fundamentals
- Understanding of compliance and governance concepts
- Experience with enterprise IT or model deployment environments
Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
