Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods
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Abstract (arXiv)
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.
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References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
- 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
- 2022Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertisecited by 8
- 2022Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference Metricscited by 2
- 2023On the stability, correctness and plausibility of visual explanation methods based on feature importancecited by 2
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