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

arXiv: https://arxiv.org/abs/2311.12481 · HTML: https://mlsanity.com/paper/2311.12481/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps](https://mlsanity.com/paper/2404.16042/) (2024)
- [Emergent, not Immanent: A Baradian Reading of Explainable AI](https://mlsanity.com/paper/2601.15029/) (2026)
- [Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification](https://mlsanity.com/paper/2608.15731/) (2026)

## Abstract (arXiv)

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.