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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory drivers such as the EU AI Act and GDPR
- Ollama’s role in enterprise AI governance
Bias Detection and Mitigation
- Recognizing bias in model outputs
- Techniques for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Crafting prompts for safety and reliability
- Addressing risks associated with unsafe or harmful outputs
- Applying alignment techniques for enterprise use cases
Content Filtering and Moderation
- Building content filtering pipelines
- Establishing moderation safeguards
- Striking a balance between user experience and compliance obligations
Governance Workflows
- Defining 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
- Mechanisms for audit readiness and reporting
Case Studies and Best Practices
- Enterprise deployments adhering to responsible AI principles
- Insights gained from real-world governance challenges
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Foundational knowledge of AI and ML concepts
- Familiarity with compliance and governance frameworks
- Background in enterprise IT or model deployment environments
Intended Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
