On The Stability of Interpretable Models
This work addresses the reliability of interpretable models for oversight agents, but it is incremental as it focuses on experimental evaluation without introducing new methods.
The study investigated the stability of interpretable models like decision trees and linear models with respect to feature, instance, and model selection, finding that biases in data collection and preparation can severely affect their accountability, highlighting the need for stability assessments.
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.