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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core principles of fairness, accountability, and transparency
- Key regulatory drivers, including the EU AI Act and GDPR
- The specific role of Ollama in enterprise AI governance
Detecting and Mitigating Bias
- Identifying potential biases in model outputs
- Effective strategies for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Model Alignment
- Crafting prompts for safety and reliability
- Reducing risks associated with unsafe or harmful outputs
- Applying alignment techniques tailored for enterprise applications
Content Filtering and Moderation
- Building effective content filtering pipelines
- Implementing robust moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing governance frameworks specific to Ollama
- Integrating workflows with existing compliance systems
- Defining model approval and audit procedures
Logging, Traceability, and Auditability
- Implementing secure logging practices for AI systems
- Ensuring full traceability of model decisions
- Preparing for audits and establishing reporting mechanisms
Case Studies and Industry Best Practices
- Analyzing enterprise deployments that adhere to responsible AI principles
- Learning from real-world governance challenges and failures
- Cultivating sustainable and ethical AI practices
Course Summary and Path Forward
Requirements
- Solid understanding of AI and ML fundamentals
- Knowledge of compliance and governance frameworks
- Practical experience in enterprise IT or model deployment environments
Intended Audience
- AI Ethics Leads
- Compliance Officers
- Legal and Regulatory Engineers
- Enterprise Architects
