Responsible AI for Developers: Interpretability & Transparency

This course is part of Responsible AI for Developers Specialization

Instructor: Google Cloud Training

What you'll learn

  •   Define interpretability and transparency as it relates to AI
  •   Describe the importance of interpretability and transparency in AI
  •   Explore the tools and techniques used to achieve interpretability and transparency in AI

    • Skills you'll gain

  •   Data-Driven Decision-Making
  •   Interoperability
  •   Data Ethics
  •   Artificial Intelligence
  •   Visualization (Computer Graphics)
  •   Applied Machine Learning
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Testability
  •   Machine Learning
  •   Technical Communication

    • There are 4 modules in this course

    This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

    AI Interpretability & Transparency

    Course Summary

    Course Resources

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    Developing Explainable AI (XAI)

    Responsible AI for Developers: Fairness & Bias

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