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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
- Techniques for identifying bias in model outputs.
- Strategies for reducing bias and enhancing fairness.
- Assessing model performance using fairness metrics.
Safe Prompting and Alignment
- Prompt engineering for safety and reliability.
- Mitigating risks associated with unsafe or harmful outputs.
- Alignment techniques suited for enterprise applications.
Content Filtering and Moderation
- Architecting content filtering pipelines.
- Implementing moderation safeguards.
- Striking a balance between user experience and compliance requirements.
Governance Workflows
- Establishing governance frameworks specifically for 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.
- Preparing for audits and establishing reporting mechanisms.
Case Studies and Best Practices
- Enterprise deployments that adhere to responsible AI principles.
- Insights gained from real-world governance challenges.
- Cultivating sustainable and ethical AI practices.
Summary and Next Steps
Requirements
- A solid grasp of AI/ML fundamentals.
- Familiarity with compliance and governance concepts.
- Hands-on experience with enterprise IT or model deployment environments.
Target Audience
- AI ethics leads.
- Compliance officers.
- Legal and regulatory engineers.
- Enterprise architects.
