Explainable artificial intelligence (XAI) is concerned with the development of techniques that can enhance the interpretability, accountability and transparency of artificial intelligence and, in particular, machine learning algorithms and models, especially black box ones, such as artificial neural networks, so that these can also be adopted in areas, like healthcare, where the interpretability and understanding of the results (e.g. classifications) is required.

Which XAI techniques are there?


There are a few XAI techniques that are (partially) agnostic to the model to be interpreted

There are also ML models that are not considered black boxes and that are thus more interpretable than black boxes, such as

  • linear models (e.g. linear regression)
  • decision trees
  • naive Bayes (and, in general, Bayesian networks)

For a more complete list of such techniques and models, have a look at the online book Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, by Christoph Molnar, which attempts to categorise and present the main XAI techniques.

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