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Alec Lamens1, Jürgen Bajorath1,2

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This summary is machine-generated.

Explainable artificial intelligence (XAI) methods for feature attribution show inconsistent results. Comparing Shapley value variants revealed distinct feature importance distributions, highlighting the need for consistency checks in machine learning explanations.

Keywords:
Shapley valuesapproximation methodscompound activity predictionfeature attributionmachine learningmodel explanation

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Area of Science:

  • Computational chemistry and cheminformatics
  • Machine learning and artificial intelligence
  • Explainable artificial intelligence (XAI)

Background:

  • Feature attribution methods in explainable artificial intelligence (XAI) quantify feature importance for machine learning model predictions.
  • The consistency of explanations derived from different attribution methods has been under-investigated, particularly in molecular machine learning.
  • Shapley value formalism is a popular game theory-based approach for feature attribution in machine learning.

Purpose of the Study:

  • To systematically compare the consistency of model explanations generated by different feature attribution methods in molecular machine learning.
  • To investigate whether methodological variants of Shapley value calculations yield similar feature importance distributions for accurate predictions.

Main Methods:

  • Generated a test system with highly accurate compound activity predictions using various machine learning models and targets.
  • Computed explanations using methodological variants of the Shapley value formalism (model-agnostic and model-specific).
  • Performed global statistical analysis to characterize and compare feature importance distributions using diverse measures.

Main Results:

  • Methodological variants for Shapley value calculations unexpectedly produced distinct feature importance distributions, even for highly accurate predictions.
  • There was minimal agreement between feature importance rankings generated by alternative model explanation methods.
  • The study revealed significant discrepancies in feature attribution outcomes depending on the specific Shapley value implementation used.

Conclusions:

  • Feature importance-based explanations derived from different attribution methods can be inconsistent.
  • The choice of Shapley value calculation method significantly impacts the resulting feature importance distributions.
  • It is crucial to include an assessment of explanation consistency using alternative methods when interpreting machine learning predictions.