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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory frameworks driving responsible AI (such as the EU AI Act and GDPR)
- Ollama's role in enterprise AI governance strategies
Detecting and Mitigating Bias
- Recognizing bias in 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
- Addressing risks associated with unsafe or harmful outputs
- Applying alignment techniques for enterprise use cases
Content Filtering and Moderation
- Constructing effective content filtering pipelines
- Incorporating moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing governance frameworks specific to Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Audit Readiness
- Best practices for secure logging in AI systems
- Ensuring traceability of model decisions
- Mechanisms for audit readiness and reporting
Case Studies and Best Practices
- Examples of enterprise deployments adhering to responsible AI principles
- Key lessons from real-world governance challenges
- Culturing sustainable and ethical AI practices
Summary and Future Directions
Requirements
- Basic knowledge of AI/ML fundamentals
- Awareness of compliance and governance concepts
- Background in enterprise IT or model deployment environments
Intended Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
