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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory drivers influencing responsible AI (such as the EU AI Act, GDPR, and other standards)
- The role of Ollama in enterprise-level AI governance
Bias Detection and Mitigation
- Methods for identifying bias in model outputs
- Techniques for reducing bias and enhancing fairness
- Assessing model performance using specific fairness metrics
Safe Prompting and Alignment
- Designing prompts that prioritize safety and reliability
- Strategies to mitigate risks associated with unsafe or harmful outputs
- Applying alignment techniques tailored for enterprise applications
Content Filtering and Moderation
- Architecting efficient content filtering pipelines
- Deploying effective moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing comprehensive governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Defining model approval and audit procedures
Logging, Traceability, and Auditability
- Implementing secure logging practices for AI systems
- Ensuring traceability of model-driven decisions
- Maintaining audit readiness and effective reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments adhering to responsible AI principles
- Insights gained from real-world governance challenges
- Developing sustainable and ethical AI practices
Summary and Next Steps
Requirements
- A solid grasp of AI/ML fundamentals
- Knowledge of compliance and governance frameworks
- Practical experience in enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
