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Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Key regulatory drivers for responsible AI (such as the EU AI Act and GDPR)
- The role of Ollama 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
- Designing prompts that ensure safety and reliability
- Mitigating risks associated with unsafe or harmful outputs
- Applying alignment techniques suitable for enterprise applications
Content Filtering and Moderation
- Architecting content filtering pipelines
- Implementing safeguards for content moderation
- Striking a balance between user experience and compliance mandates
Governance Workflows
- Establishing governance frameworks for Ollama environments
- Integrating workflows with existing compliance systems
- Managing model approval and audit procedures
Logging, Traceability, and Auditability
- Adopting secure logging practices for AI systems
- Ensuring traceability of model decisions
- Preparing for audits through robust reporting mechanisms
Case Studies and Best Practices
- Examining enterprise deployments grounded in responsible AI principles
- Extracting lessons from real-world governance failures
- Cultivating sustainable and ethical AI practices
Summary and Next Steps
Requirements
- Foundational understanding of AI and ML concepts
- Familiarity with governance and compliance principles
- Practical experience in enterprise IT or model deployment environments
Target Audience
- AI ethics leads
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
14 Hours