A Survey on the Explainability of Supervised Machine Learning

TL;DR

This survey reviews the principles, methodologies, and recent advances in explainable supervised machine learning, emphasizing the importance of transparency and interpretability in sensitive domains like healthcare and finance.

Contribution

It provides a comprehensive classification of explainable SML approaches, clarifies key definitions, and discusses future research directions.

Findings

Classified existing explainability methods

Highlighted importance in sensitive domains

Presented an illustrative case study

Abstract

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or fifinance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.