Can I Trust the Explanations? Investigating Explainable Machine Learning
  Methods for Monotonic Models
Dangxing Chen
In recent years, explainable machine learning methods have been very
successful. Despite their success, most explainable machine learning methods
are applied to black-box models without any domain knowledge. By incorporating
domain knowledge, science-informed machine learning models have demonstrated
better generalization and interpretation. But do we obtain consistent
scientific explanations if we apply explainable machine learning methods to
science-informed machine learning models? This question is addressed in the
context of monotonic models that exhibit three different types of monotonicity.
To demonstrate monotonicity, we propose three axioms. Accordingly, this study
shows that when only individual monotonicity is involved, the baseline Shapley
value provides good explanations; however, when strong pairwise monotonicity is
involved, the Integrated gradients method provides reasonable explanations on
average.