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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory influences on responsible AI (including the EU AI Act, GDPR, etc.)
- The contribution of Ollama to 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 Model Alignment
- Crafting prompts for safety and reliability
- Mitigating risks associated with unsafe or harmful outputs
- Applying alignment techniques for enterprise-grade applications
Content Filtering and Moderation
- Structuring content filtering pipelines
- Implementing moderation safeguards
- Balancing user experience against compliance obligations
Governance Frameworks
- Establishing governance structures specific to Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Audit Readiness
- Implementing secure logging for AI systems
- Ensuring traceability of model decisions
- Preparing for audits and establishing reporting mechanisms
Case Studies and Industry Best Practices
- Examples of enterprise deployments adhering to responsible AI principles
- Insights derived from real-world governance challenges
- Cultivating sustainable and ethical AI operations
Recap and Future Directions
Requirements
- A solid grasp of AI/ML fundamentals
- Knowledge of compliance and governance frameworks
- Practical experience in enterprise IT or model deployment contexts
Target Audience
- AI ethics specialists
- Compliance professionals
- Legal and regulatory engineers
- Enterprise architects
