Explainable AI

Artificial Intelligence    |    Intermediate
  • 11 Videos | 41m 57s
  • Includes Assessment
  • Earns a Badge
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The inner workings of many deep learning systems are complicated, if not impossible, for the human mind to comprehend. Explainable Artificial Intelligence (XAI) aims to provide AI experts with transparency into these systems. In this course, you'll describe what Explainable AI is, how to use it, and the data structures behind XAI's preferred algorithms. Next, you'll explore the interpretability problem and today's state-of-the-art solutions to it. You'll identify XAI regulations, define the "right to explanation", and illustrate real-world examples where this has been applicable. You'll move on to recognize both the Counterfactual and Axiomatic methods, distinguishing their pros and cons. You'll investigate the intelligible models method, along with the concepts of monotonicity and rationalization. Finally, you'll learn how to use a Generative Adversarial Network.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    recognize what Explainable AI is and its significance
    define the Interpretability Problem and its importance
    define 'right to explanation' regulations and identify real-world use cases
    identify the meaning and application of the counterfactual method
    identify the meaning and application of axiomatic attribution
  • define the meaning and recognize the purpose of intelligible models
    define the meaning and recognize the real-world use of monotonicity
    describe the meaning and application of rationalization
    describe the meaning and application of feature visualization
    summarize the key concepts covered in this course

IN THIS COURSE

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