Intepretable Local Explanations Through Genetic Programming

As machine learning models become increasingly prevalent in everyday life, there is a growing demand for explanation of the predictions generated by these models. However, most models used by companies are black-boxes in nature, without the capacity to provide explanations to users. This reduces public trust in these models, and exists as a barrier to adoption of machine learning. Research into providing explanations to users has shown that local explanation techniques provide more acceptable explanations to users than attempting to explain an entire model, as a user often does not need to understand the entirety of a model. This work builds on prior work in the field to produce a competitive method for high-fidelity local explanations utilising genetic programming. Two different data representations targeted towards both users with and without machine learning experience are evaluated. The experimental results show comparable fidelity to the state-of-the art, while exhibiting more comprehensible explanations due to including fewer features in each explanation. The method enables decomposable explanations that are easy to interpret, while still capturing non-linear relationships in the original model.