The non-linear nature of the cost of comprehensibility

Abstract

A key challenge in Artificial Intelligence (AI) has been the potential trade-off between the accuracy and comprehensibility of machine learning models, as that also relates to their safe and trusted adoption. While there has been a lot of talk about this trade-off, there is no systematic study that assesses to what extent it exists, how often it occurs, and for what types of datasets. Based on the analysis of 90 benchmark classification datasets, we find that this trade-off exists for most (69%) of the datasets, but that somewhat surprisingly for the majority of cases it is rather small while for only a few it is very large. Comprehensibility can be enhanced by adding yet another algorithmic step, that of surrogate modelling using so-called ‘explainable’ models. Such models can improve the accuracy-comprehensibility trade-off, especially in cases where the black box was initially better. Finally, we find that dataset characteristics related to the complexity required to model the dataset, and the level of noise, can significantly explain this trade-off and thus the cost of comprehensibility. These insights lead to specific guidelines on how and when to apply AI algorithms when comprehensibility is required.