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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
- The role of Ollama in enterprise AI governance
Bias Detection and Mitigation
- Identifying biases in model outputs
- Strategies for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Designing prompts for safety and reliability
- Mitigating the risks of unsafe or harmful outputs
- Applying alignment techniques for enterprise applications
Content Filtering and Moderation
- Building content filtering pipelines
- Implementing moderation safeguards
- Balancing user experience with compliance requirements
Governance Workflows
- Defining governance frameworks for Ollama
- Integrating workflows with compliance systems
- Model approval and audit procedures
Logging, Traceability, and Auditability
- Secure logging practices for AI systems
- Ensuring the traceability of model decisions
- Maintaining audit readiness and reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments adhering to responsible AI principles
- Insights gained from real-world governance failures
- Developing sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Fundamental knowledge of AI/ML concepts
- Working familiarity with compliance and governance frameworks
- Experience in enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
