Each week, we select a recently published Open Access article to feature. This week’s article comes from WIREs Data Mining and Knowledge Discovery and provides an overview of causability and explainability of artificial intelligence in medicine.

The article’s abstract is given below, with the full article available to read here.

Holzinger, A, Langs, G, Denk, H, Zatloukal, K, Müller, H. Causability and explainability of artificial intelligence in medicine. WIREs Data Mining Knowl Discov. 2019; 9:e1312. https://doi.org/10.1002/widm.1312

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black‐box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use‐case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system.