# Explainable Machine Learning for Scientific Insights and Discoveries

## TL;DR

This paper reviews how explainable machine learning techniques are applied in natural sciences to derive scientific insights, emphasizing transparency, interpretability, and domain knowledge integration.

## Contribution

It provides a comprehensive survey of recent scientific works that combine explainable machine learning with domain knowledge in natural sciences.

## Key findings

- Explainable ML enhances scientific understanding.
- Integration of domain knowledge improves model reliability.
- Survey covers recent advances in scientific applications.

## Abstract

Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article we review explainable machine learning in view of applications in the natural sciences and discuss three core elements which we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.