Contributo in atti di convegno, 2019, ENG, 10.1109/IJCNN.2019.8852158
Guidotti R.; Ruggieri S.
CNR-ISTI, Pisa, Italy; CNR-ISTI - University of Pisa, Pisa, Italy
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.
IJCNN 2019 - International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14-19 July, 2019
Classifiers, Interpretability, Model Stability
Ruggieri Salvatore, Guidotti Riccardo
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 417416
Year: 2019
Type: Contributo in atti di convegno
Creation: 2020-02-20 14:45:56.000
Last update: 2021-01-12 22:24:24.000
CNR authors
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:417416
DOI: 10.1109/IJCNN.2019.8852158
Scopus: 2-s2.0-85073207373
ISI Web of Science (WOS): 000530893803090