Title: A review of possible effects of cognitive biases on interpretation of rule-based machine learning models,
Abstract: 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.