The Disagreement Problem in Faithfulness Metrics
Brian Barr Noah Fatsi Leif Hancox-Li Peter Richter Daniel Proano Caleb Mok
The field of explainable artificial intelligence (XAI) aims to explain how
black-box machine learning models work. Much of the work centers around the
holy grail of providing post-hoc feature attributions to any model
architecture. While the pace of innovation around novel methods has slowed
down, the question remains of how to choose a method, and how to make it fit
for purpose. Recently, efforts around benchmarking XAI methods have suggested
metrics for that purpose -- but there are many choices. That bounty of choice
still leaves an end user unclear on how to proceed. This paper focuses on
comparing metrics with the aim of measuring faithfulness of local explanations
on tabular classification problems -- and shows that the current metrics don't
agree; leaving users unsure how to choose the most faithful explanations.