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LinkedIn Learning

Foundations of Responsible AI

via LinkedIn Learning

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Overview

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Explore a practical framework for implementing AI practices in a way that bridges the gap between high-level AI ethics principles and day-to-day technical decisions.

Syllabus

Introduction
  • Intro to the course
1. Reframing Responsible AI for Technical Teams
  • Introduction to course objectives
  • The business case for early integration of responsible AI
  • Engineers as ethics first responders
  • Mapping technical choices to ethical principles
2. Where Technical Decisions Shape Responsible AI
  • Critical decision points in AI development
  • Translating values into code
  • Integrating RAI into existing workflows
3. Architecture Decisions That Matter
  • Model selection trade-offs
  • Building explainability from day one
  • Privacy-preserving architectures
4. Monitoring and Testing for Responsible AI
  • Metrics for responsible AI
  • Designing effective monitoring systems
  • Systematic approaches to bias detection
  • Balancing automation and human judgment
  • Comprehensive testing for ethical robustness
5. Team Structures and Processes
  • Distributing RAI expertise in teams
  • Documentation that builds trust
  • Lightweight but effective review checkpoints
  • Breaking down communication barriers
6. Risk Assessment and Decision Frameworks
  • Solution-focused risk assessment
  • Early warning signs in system behavior
  • Frameworks for complex trade-off decisions
  • Learning from real-world RAI implementations
  • A roadmap for the future
Conclusion
  • What's next

Taught by

Ayodele Odubela

Reviews

4.6 rating at LinkedIn Learning based on 272 ratings

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