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Explainable artificial intelligence (XAI) methods lack ground truth. In itsplace, method developers have relied on axioms to determine desirableproperties for their explanations' behavior. For high stakes uses of machinelearning that require explainability, it is not sufficient to rely on axioms asthe implementation, or its usage, can fail to live up to the ideal. As aresult, there exists active research on validating the performance of XAImethods. The need for validation is especially magnified in domains with areliance on XAI. A procedure frequently used to assess their utility, and tosome extent their fidelity, is an ablation study. By perturbing the inputvariables in rank order of importance, the goal is to assess the sensitivity ofthe model's performance. Perturbing important variables should correlate withlarger decreases in measures of model capability than perturbing less importantfeatures. While the intent is clear, the actual implementation details have notbeen studied rigorously for tabular data. Using five datasets, three XAImethods, four baselines, and three perturbations, we aim to show 1) how varyingperturbations and adding simple guardrails can help to avoid potentially flawedconclusions, 2) how treatment of categorical variables is an importantconsideration in both post-hoc explainability and ablation studies, and 3) howto identify useful baselines for XAI methods and viable perturbations forablation studies.

BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence