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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, such as the EU AI Act and GDPR
- Ollama's role in enterprise AI governance strategies
Bias Detection and Mitigation
- Techniques for identifying bias in model outputs
- Strategies to reduce bias and enhance fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Prompt engineering for safety and reliability
- Strategies to mitigate risks associated with unsafe or harmful outputs
- Alignment techniques tailored for enterprise applications
Content Filtering and Moderation
- Architecting effective content filtering pipelines
- Implementing robust moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Formulating governance frameworks specifically for Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Auditability
- 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
- Insights gained from real-world governance challenges
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- A solid grasp of AI and ML fundamentals
- Working knowledge of compliance and governance concepts
- Practical experience in enterprise IT or model deployment environments
Target Audience
- AI Ethics Leads
- Compliance Officers
- Legal and Regulatory Engineers
- Enterprise Architects
