However, further comparisons, specifically with regards to randomization should help improve robustness in XAI methods. Although, as Adebayo et al. discussed, randomization can indicate architecture-based priors of XAI, XAI should reflect learned parameters to explain model behaviour. Methods to increase XAI robustness to randomized models can include but are not limited to creating models to investigate ML models, such as Schwab and Karlen , contrasting findings to randomized models predictions as background instead of empty samples, and counterfactual explanations, such as the recent study from Fradkin et al. .