XAI: eXplainable AI

Table of Contents

1. Black Box AI

  • Black box AI: AI produces insights based on a data set, but the end-user doesn’t know how
    • Many machine learning and deep learning models share ‘black box’ problem
    • AI does not provide reasons behind the decision or prediction it makes
    • The reliability of AI models may be questioned



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  • XAI which humans can understand the decisions or predictions made by the AI

Why XAI?

  • XAI can be used to increase the interpretability of AI by enabling description of the expected outcome and potential bias of the model
  • Depending on the AI performance, XAI results can be used in various ways:
    • AI performance < Human performance
      • XAI suggests improvement directions for AI models
    • AI performance ≈ Human performance
      • XAI identifies the principles behind AI model learning
    • AI performance > Human performance
      • XAI enables acquiring new knowledge from AI