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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 (including the EU AI Act and GDPR)
- The role of Ollama in enterprise AI governance
Bias Detection and Mitigation
- Recognizing bias in model outputs
- Approaches to reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Designing prompts for safety and reliability
- Addressing risks associated with unsafe or harmful outputs
- Applying alignment techniques for enterprise-level applications
Content Filtering and Moderation
- Building content filtering pipelines
- Implementing moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Model approval processes and audit procedures
Logging, Traceability, and Auditability
- Secure logging practices for AI systems
- Ensuring traceability of model decisions
- Preparing for audits and implementing reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments adhering to responsible AI principles
- Insights gained from real-world governance failures
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Foundational knowledge of AI/ML concepts
- Basic understanding of compliance and governance frameworks
- Practical experience with enterprise IT or model deployment environments
Target Audience
- AI ethics leaders
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
