EVALUATING FAITHFULNESS OF EXPLAINABLE AI FOR MEDICAL DECISION MAKING

Abstract

Methods and systems include fine-tuning a classifier while masking part of a training dataset to cause a distribution of the classifier to match a distribution of an explainer model. A performance of the explainer model is determined using the fine-tuned classifier to ensure that the explainer has an above-threshold fidelity. A downstream task is performed using the classifier and the explainer model.

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