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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 function of Ollama in enterprise AI governance.
Bias Detection and Mitigation
- Recognizing bias in model outputs.
- Techniques for reducing bias and enhancing fairness.
- Assessing model performance using fairness metrics.
Safe Prompting and Alignment
- Crafting prompts for safety and reliability.
- Mitigating risks associated with unsafe or harmful outputs.
- Applying alignment techniques in enterprise contexts.
Content Filtering and Moderation
- Structuring content filtering pipelines.
- Applying moderation safeguards.
- Striking a balance between user experience and compliance obligations.
Governance Workflows
- Establishing governance frameworks for Ollama.
- Integrating workflows with existing compliance systems.
- Procedures for model approval and auditing.
Logging, Traceability, and Auditability
- Secure logging practices for AI systems.
- Ensuring traceability of model decisions.
- Maintaining audit readiness and reporting mechanisms.
Case Studies and Best Practices
- Enterprise implementations adhering to responsible AI principles.
- Insights gained 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 frameworks.
- Experience in enterprise IT or model deployment environments.
Target Audience
- AI ethics specialists.
- Compliance officers.
- Legal and regulatory engineers.
- Enterprise architects.
