Using ontologies to enhance human understandability of global post-hoc explanations of black-box models

The interest in explainable artificial intelligence has grown strongly in recent years because of the need to convey safety and trust in the ‘how’ and ‘why’ of automated decision-making to users. While a plethora of approaches has been developed, only a few focus on how to use domain knowledge and how this influences the understanding of explanations by users. In this paper, we show that by using ontologies we can improve the human understandability of global post-hoc explanations, presented in the form of decision trees. In particular, we introduce TREPAN Reloaded, which builds on TREPAN, an algorithm that extracts surrogate decision trees from black-box models. TREPAN Reloaded includes ontologies, that model domain knowledge, in the process of extracting explanations to improve their understandability. We tested the understandability of the extracted explanations by humans in a user study with four different tasks. We evaluate the results in terms of response times and correctness, subjective ease of understanding and confidence, and similarity of free text responses. The results show that decision trees generated with TREPAN Reloaded, taking into account domain knowledge, are significantly more understandable throughout than those generated by standard TREPAN. The enhanced understandability of post-hoc explanations is achieved with little compromise on the accuracy with which the surrogate decision trees replicate the behaviour of the original neural network models.