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Duration 14 hours
Course Outline
Overview of Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory influences on responsible AI (including the EU AI Act, GDPR, and others)
- Ollama’s function in enterprise AI governance
Identifying and Mitigating Bias
- Recognising bias in model outputs
- Tactics for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Crafting prompts for safety and reliability
- Reducing risks associated with unsafe or harmful outputs
- Applying alignment techniques in enterprise contexts
Content Filtering and Moderation
- Architecting content filtering pipelines
- Implementing moderation safeguards
- Striking a balance between user experience and compliance obligations
Governance Workflows
- Establishing governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Defining model approval and audit procedures
Logging, Traceability, and Auditability
- Best practices for secure logging in AI systems
- Ensuring traceability of model decisions
- Maintaining audit readiness and reporting capabilities
Case Studies and Best Practices
- Enterprise implementations adhering to responsible AI principles
- Insights gained from real-world governance challenges
- Cultivating sustainable and ethical AI practices
Recap and Path Forward
Requirements
- Foundational knowledge of AI/ML concepts
- Acquaintance with compliance and governance frameworks
- Practical experience in enterprise IT or model deployment settings
Intended Audience
- AI ethics specialists
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
