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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core principles of fairness, accountability, and transparency
- Key regulatory drivers for responsible AI (including the EU AI Act, GDPR, etc.)
- Ollama's role in shaping enterprise AI governance strategies
Bias Detection and Mitigation Strategies
- Techniques for identifying bias in model outputs
- Approaches to reduce bias and enhance fairness
- Assessing model performance using established fairness metrics
Safe Prompting and Model Alignment
- Designing prompts for enhanced safety and reliability
- Strategies to mitigate risks associated with unsafe or harmful outputs
- Applying alignment techniques suited for enterprise applications
Content Filtering and Moderation
- Architecting effective content filtering pipelines
- Implementing robust moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Defining structured governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Establishing model approval and audit procedures
Logging, Traceability, and Auditability
- Adopting secure logging practices for AI systems
- Ensuring full traceability of model decision-making processes
- Maintaining audit readiness and streamlined reporting mechanisms
Case Studies and Industry Best Practices
- Examining enterprise deployments that adhere to responsible AI principles
- Learning from real-world governance challenges and failures
- Cultivating sustainable and ethical AI operational practices
Summary and Strategic Next Steps
Requirements
- A solid grasp of AI/ML fundamentals
- Familiarity with compliance and governance frameworks
- Hands-on experience with enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
