Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods
<|reference_start|>Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods: This paper investigates the use of Mean Opinion Score (MOS), a common image quality metric, as a user-centric evaluation metric for XAI post-hoc explainers. To measure the MOS, a user experiment is proposed, which has been conducted with explanation maps of intentionally distorted images. Three methods from the family of feature attribution methods - Gradient-weighted Class Activation Mapping (Grad-CAM), Multi-Layered Feature Explanation Method (MLFEM), and Feature Explanation Method (FEM) - are compared with this metric. Additionally, the correlation of this new user-centric metric with automatic metrics is studied via Spearman's rank correlation coefficient. MOS of MLFEM shows the highest correlation with automatic metrics of Insertion Area Under Curve (IAUC) and Deletion Area Under Curve (DAUC). However, the overall correlations are limited, which highlights the lack of consensus between automatic and user-centric metrics.<|reference_end|>
@article{yu2024mean,
    title={Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods},
    author={Hyeon Yu, Jenny Benois-Pineau, Romain Bourqui, Romain Giot, Alexey
  Zhukov},
    journal={arXiv preprint arXiv:2407.20427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20427},
    primaryClass={cs.CV eess.IV}
}