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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Key regulatory drivers for responsible AI, such as the EU AI Act and GDPR
- The role of Ollama in shaping enterprise AI governance
Bias Detection and Mitigation
- Methods for identifying bias within model outputs
- Strategies to reduce bias and enhance fairness
- Assessing model performance using specific fairness metrics
Safe Prompting and Alignment
- Crafting prompts for enhanced safety and reliability
- Techniques to mitigate risks associated with unsafe or harmful outputs
- Alignment methods tailored for enterprise applications
Content Filtering and Moderation
- Architecting content filtering pipelines
- Implementing robust 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 Auditability
- Best practices for secure logging in AI systems
- Ensuring traceability of model decisions
- Maintaining audit readiness and effective reporting mechanisms
Case Studies and Best Practices
- Examples of 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 fundamental AI/ML concepts
- Knowledge of compliance and governance frameworks
- Practical experience with enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
