01264nas a2200109   4500008004100000245005200041210005200093520088000145100002301025700002401048856008201072       2018                        eng d00aAssessing the Stability of Interpretable Models0 aAssessing the Stability of Interpretable Models3 aInterpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/assessing-stability-interpretable-models