Responsible AI

Responsible AI

Learn how to use features in Dataiku to keep the outputs of AI aligned with Responsible AI principles.

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About this course

Curriculum90 min

  • Introduction to Responsible AI
  • Concept | Responsible AI
  • Concept | Dangers of irresponsible AI
  • Concept | Responsible AI in the data science practice
  • Tutorial | Get started
  • Identifying Data Biases
  • Important caveats
  • Concept | Basics of bias
  • Tutorial | Question the data
  • Tutorial | Observe the data
  • Tutorial | Test the data
  • Model Fairness & Interpretability
  • Concept | Model fairness
  • Tutorial | Model fairness metrics
  • Concept | Evaluating group fairness
  • Tutorial | Design for group fairness
  • Concept | Interpretability
  • Tutorial | Interpretability techniques
  • Model Explainability & Reporting
  • Concept | Model transparency
  • Concept | Deployment biases
  • Tutorial | Model explainability
  • Tutorial | Reporting strategies
  • Wrap Up
  • Tell Us What You Think
  • Wrap Up | Responsible AI

About this course

Curriculum90 min

  • Introduction to Responsible AI
  • Concept | Responsible AI
  • Concept | Dangers of irresponsible AI
  • Concept | Responsible AI in the data science practice
  • Tutorial | Get started
  • Identifying Data Biases
  • Important caveats
  • Concept | Basics of bias
  • Tutorial | Question the data
  • Tutorial | Observe the data
  • Tutorial | Test the data
  • Model Fairness & Interpretability
  • Concept | Model fairness
  • Tutorial | Model fairness metrics
  • Concept | Evaluating group fairness
  • Tutorial | Design for group fairness
  • Concept | Interpretability
  • Tutorial | Interpretability techniques
  • Model Explainability & Reporting
  • Concept | Model transparency
  • Concept | Deployment biases
  • Tutorial | Model explainability
  • Tutorial | Reporting strategies
  • Wrap Up
  • Tell Us What You Think
  • Wrap Up | Responsible AI