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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Key regulatory drivers, including the EU AI Act and 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
- Alignment strategies for enterprise-level applications
Content Filtering and Moderation
- Structuring content filtering pipelines
- Deploying moderation safeguards
- Striking a balance between user experience and compliance mandates
Governance Workflows
- Establishing governance frameworks specifically for Ollama
- Integrating workflows with existing compliance systems
- Processes for model approval and auditing
Logging, Traceability, and Auditability
- Secure logging protocols for AI systems
- Ensuring traceability of model decisions
- Preparing for audits and implementing reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments guided by responsible AI principles
- Insights gained from real-world governance challenges
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Foundational knowledge of AI/ML concepts
- Basic understanding of compliance and governance frameworks
- Hands-on experience with enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
