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Course Outline

Introduction to Responsible AI

  • Core principles of fairness, accountability, and transparency.
  • Regulatory drivers influencing responsible AI (e.g., EU AI Act, GDPR).
  • The role of Ollama in enterprise AI governance.

Bias Detection and Mitigation

  • Identifying bias within model outputs.
  • Strategies for reducing bias and enhancing fairness.
  • Evaluating 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 tailored for enterprise applications.

Content Filtering and Moderation

  • Architecting content filtering pipelines.
  • Implementing moderation safeguards.
  • Balancing user experience with regulatory compliance requirements.

Governance Workflows

  • Establishing governance frameworks for Ollama.
  • Integrating workflows with existing compliance systems.
  • Procedures for model approval and auditing.

Logging, Traceability, and Auditability

  • Secure logging practices for AI systems.
  • Ensuring traceability of model decisions.
  • Preparing for audits and establishing reporting mechanisms.

Case Studies and Best Practices

  • Enterprise deployments guided by responsible AI principles.
  • Lessons learned from real-world governance failures.
  • Building sustainable and ethical AI practices.

Summary and Next Steps

Requirements

  • Foundational knowledge of AI/ML concepts.
  • Understanding of compliance and governance principles.
  • Experience with enterprise IT environments or model deployment.

Target Audience

  • AI ethics leads.
  • Compliance officers.
  • Legal and regulatory engineers.
  • Enterprise architects.
 14 Hours

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