On Trustworthy Rule-Based Models and Explanations
This work tackles the critical issue of ensuring reliable explanations for human decision-makers in high-stakes applications, though it is incremental in analyzing existing problems rather than proposing new solutions.
The paper addresses the problem of untrustworthy explanations from rule-based ML models in high-risk domains by analyzing negative facets like redundancy and overlap, concluding that widely used learning tools produce rule sets with these issues.
A task of interest in machine learning (ML) is that of ascribing explanations to the predictions made by ML models. Furthermore, in domains deemed high risk, the rigor of explanations is paramount. Indeed, incorrect explanations can and will mislead human decision makers. As a result, and even if interpretability is acknowledged as an elusive concept, so-called interpretable models are employed ubiquitously in high-risk uses of ML and data mining (DM). This is the case for rule-based ML models, which encompass decision trees, diagrams, sets and lists. This paper relates explanations with well-known undesired facets of rule-based ML models, which include negative overlap and several forms of redundancy. The paper develops algorithms for the analysis of these undesired facets of rule-based systems, and concludes that well-known and widely used tools for learning rule-based ML models will induce rule sets that exhibit one or more negative facets.