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.

科学信息化机器学习模型中应用可解释机器学习方法是否会得到一致的科学解释是本研究的关键问题，通过引入领域知识，提出了描述单调性的三个公理，并得出结论当仅涉及个体单调性时，基准 Shapley 值提供了良好的解释，但当涉及强对数单调性时，集成梯度方法在平均上提供了合理的解释。