ALEX: Active Learning based Enhancement of a Model's Explainability
This addresses the need for more interpretable machine learning models in data-driven applications, but it is incremental as it builds on existing active learning heuristics.
The paper tackles the problem of improving model interpretability through active learning by proposing a selection function that trains an explainer model to favor instances where different data parts explain predictions, with initial experiments showing encouraging trends for more effective and explainable classifiers.
An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner. While standard AL heuristics, such as selecting those points for annotation for which a classification model yields least confident predictions, there has been no empirical investigation to see if these heuristics lead to models that are more interpretable to humans. In the era of data-driven learning, this is an important research direction to pursue. This paper describes our work-in-progress towards developing an AL selection function that in addition to model effectiveness also seeks to improve on the interpretability of a model during the bootstrapping steps. Concretely speaking, our proposed selection function trains an `explainer' model in addition to the classifier model, and favours those instances where a different part of the data is used, on an average, to explain the predicted class. Initial experiments exhibited encouraging trends in showing that such a heuristic can lead to developing more effective and more explainable end-to-end data-driven classifiers.