Diverse Explanations from Data-driven and Domain-driven Perspectives for
  Machine Learning Models
Sichao Li Amanda Barnard
Explanations of machine learning models are important, especially in
scientific areas such as chemistry, biology, and physics, where they guide
future laboratory experiments and resource requirements. These explanations can
be derived from well-trained machine learning models (data-driven perspective)
or specific domain knowledge (domain-driven perspective). However, there exist
inconsistencies between these perspectives due to accurate yet misleading
machine learning models and various stakeholders with specific needs, wants, or
aims. This paper calls attention to these inconsistencies and suggests a way to
find an accurate model with expected explanations that reinforce physical laws
and meet stakeholders' requirements from a set of equally-good models, also
known as Rashomon sets. Our goal is to foster a comprehensive understanding of
these inconsistencies and ultimately contribute to the integration of
eXplainable Artificial Intelligence (XAI) into scientific domains.