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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory factors driving responsible AI adoption (including the EU AI Act and GDPR)
- Ollama's specific role in enterprise AI governance
Identifying and Reducing Bias
- Detecting bias within model outputs
- Techniques for minimizing bias and enhancing fairness
- Assessing model performance using specific fairness metrics
Safe Prompting and Model Alignment
- Designing prompts that prioritize safety and reliability
- Mitigating the risk of unsafe or harmful outputs
- Applying alignment techniques suitable for enterprise environments
Content Filtering and Moderation
- Structuring efficient content filtering pipelines
- Deploying moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing governance frameworks specifically for Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Audit Trails
- Implementing secure logging protocols for AI systems
- Ensuring the traceability of model decision-making
- Preparing for audits and defining reporting mechanisms
Case Studies and Industry Best Practices
- Examples of enterprise deployments that uphold responsible AI principles
- Insights gained from real-world governance challenges
- Cultivating sustainable and ethical AI operations
Recap and Future Directions
Requirements
- Foundational knowledge of AI/ML core concepts
- Basic familiarity with compliance and governance frameworks
- Practical experience in enterprise IT or model deployment settings
Target Audience
- AI ethics specialists
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
