A review of possible effects of cognitive biases on interpretation of rule-based machine learning models

This paper investigates to what extent cognitive biases may affect human understanding of interpretable machine learning models, in particular of rules discovered from data. Twenty cognitive biases are covered, as are possible debiasing techniques that can be adopted by designers of machine learning algorithms and software. Our review transfers results obtained in cognitive psychology to the domain of machine learning, aiming to bridge the current gap between these two areas. It needs to be followed by empirical studies specifically aimed at the machine learning domain.