Interpretability is in the eye of the beholder: Human versus artificial classification of image segments generated by humans versus XAI

TL;DR

This study compares human and AI classification of image segments, revealing that interpretability varies with explanation methods, image types, and the agent interpreting, highlighting the complexity of evaluating explainable AI.

Contribution

It demonstrates that human and AI interpretability of image segments depends on the explanation method and image type, challenging the notion of universal interpretability metrics.

Findings

Grad-CAM is interpretable for indoor scenes and landscapes.

XRAI is interpretable for objects.

Human and model performance diverge on human-generated segments.

Abstract

The evaluation of explainable artificial intelligence is challenging, because automated and human-centred metrics of explanation quality may diverge. To clarify their relationship, we investigated whether human and artificial image classification will benefit from the same visual explanations. In three experiments, we analysed human reaction times, errors, and subjective ratings while participants classified image segments. These segments either reflected human attention (eye movements, manual selections) or the outputs of two attribution methods explaining a ResNet (Grad-CAM, XRAI). We also had this model classify the same segments. Humans and the model largely agreed on the interpretability of attribution methods: Grad-CAM was easily interpretable for indoor scenes and landscapes, but not for objects, while the reverse pattern was observed for XRAI. Conversely, human and model performance diverged for human-generated segments. Our results caution against general statements about interpretability, as it varies with the explanation method, the explained images, and the agent interpreting them.