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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory forces driving responsible AI adoption (e.g., EU AI Act, GDPR)
- The pivotal role of Ollama in enterprise AI governance
Bias Detection and Mitigation
- Identifying and analyzing bias in model outputs
- Strategies for reducing bias and enhancing fairness
- Assessing model performance using specific fairness metrics
Safe Prompting and Alignment
- Designing prompts for maximum safety and reliability
- Mitigating risks associated with unsafe or harmful outputs
- Applying alignment techniques suited for enterprise applications
Content Filtering and Moderation
- Building robust content filtering pipelines
- Implementing effective moderation safeguards
- Striking a balance between user experience and compliance obligations
Governance Workflows
- Defining comprehensive governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Establishing model approval and audit procedures
Logging, Traceability, and Auditability
- Implementing secure logging practices for AI systems
- Ensuring full traceability of model decision-making
- Preparing for audits and establishing reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments that successfully apply responsible AI principles
- Key takeaways from real-world governance challenges
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Foundational knowledge of AI/ML concepts
- Working familiarity with compliance and governance frameworks
- Practical experience in enterprise IT or model deployment environments
Target Audience
- AI Ethics Leaders
- Compliance Officers
- Legal and Regulatory Engineers
- Enterprise Architects
