XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance

Only recently, a trend towards the objective empirical validation of XAI methods using ground truth data has been observed (Tjoa and Guan, 2020; Li et al, 2021; Zhou et al, 2022; Arras et al, 2022; Gevaert et al, 2022; Agarwal et al, 2022). These studies are, however, limited in the extent to which they permit a quantitative assessment of explanation performance, in the breadth of XAI methods evaluated, and in the difficulty of the posed'explanation' problems. In particular, most published benchmark datasets are constructed in a way such that realistic correlations between class-dependent (e.g., the foreground or object of an image) and class-agnostic (e.g., the image background) features are excluded. In practice, such dependencies can give rise to features acting as suppressor variables. Briefly, suppressor variables have no statistical association to the prediction target on their own, yet including them may allow an ML model to remove unwanted signals (noise), which can lead to improved predictions. In the context of image or photography data, suppressor variables could be parts of the background that capture the general lighting conditions.