Lecture 1 — Introduction to XAI

XAI Course Notes

In this introduction lecture on explainability in AI, we will delve into the key topics that surround this emerging field. We will first provide an overview of the motivation for explainability, exploring how it helps us to achieve more transparent and trustworthy AI systems, particularly from a managerial perspective. We will then define some of the key terminology in the field and differentiate between black box explanation and interpretable ML. We will discuss the differences between global and local explanations, and include many examples from different fields and use cases throughout the lecture. Next, we will examine the “built-in” feature importance methods that are commonly used for regression and trees, and discuss the strengths and limitations of these methods. Overall, this lecture will provide a comprehensive introduction to explainability in AI, covering the key topics and terminology that are essential for understanding this field.

explainable AI
XAI
machine learning
ML
data science
contrafactuals
casual inference
CI
Author

Oren Bochman

Published

Sunday, March 5, 2023

Series Poster

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good

better

better

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References

  • https://www.youtube.com/watch?v=6qisPX7o-bg

Reuse

CC SA BY-NC-ND

Citation

BibTeX citation:
@online{bochman2023,
  author = {Bochman, Oren},
  title = {Lecture 1 -\/-\/- {Introduction} to {XAI}},
  date = {2023-03-05},
  url = {https://orenbochman.github.io/qa_demo1.html},
  langid = {en}
}
For attribution, please cite this work as:
Bochman, Oren. 2023. “Lecture 1 --- Introduction to XAI.” March 5, 2023. https://orenbochman.github.io/qa_demo1.html.