01172nas a2200121   4500008004100000245004500041210003800086260000900124520081200133100002300945700002400968856005800992       2019                        eng d00aOn The Stability of Interpretable Models0 aStability of Interpretable Models  bIEEE3 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. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://ieeexplore.ieee.org/abstract/document/8852158