[D] Evaluating SHAP reliability in the presence of multicollinearity

Hi, SHapley Additive exPlanations (SHAP) is a popular eXplainable Artificial Intelligence (XAI) method, popular among practitioners. I just discovered that if the covariates of an ML model are highly correlated, the SHAP values are influenced by this multicollinearity (please see the paper A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME).

This means that although ML models (e.g., Random Forest) might be robust against multicollinear covariates, one must be very careful when explaining them using SHAP. So, my questions are:

1. If one removes collinear variables for the model (using e.g., VIF), will this increase the reliability of SHAP?
2. Is there another XAI model (apart from LIME and SHAP) that can handle multicollinearity? To be more precise, I am about to use a Random Forest for a prediction task, and I am looking for R packages that provide alternative, collinearity-robust XAI models.