On The Stability of Interpretable Models

By Riccardo Guidotti, Salvatore Ruggieri. 2018

38 citations recorded by the source corpus.

Interpretable 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.

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1. Alicja Gosiewska
2. Przemyslaw Biecek
3. Mateusz Staniak
4. Christoph Molnar
5. Michal Kuzba

Citations in the corpus

• Do Not Trust Additive Explanations (2019, background): “Such tools are widely adopted, but little is said about the quality of their explanations (Guidotti and Ruggieri, 2018; Yeh et al., 2019).”