Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models

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 …

biology chemistry cs.lg data data-driven diverse domain domain knowledge future guide knowledge laboratory machine machine learning machine learning models perspective perspectives physics requirements