Researchers From Italy Introduce ‘ferret’: A Novel Python Library for Benchmarking Explainers on Transformers

Explainable AI (XAI) has seen steady growth in recent years. Innovative methods include calculating Shapley Values, quantifying backward pass gradients, occluding input sections, counterfactual input editing, and employing simpler surrogate models to explain model predictions. Despite having the same goal, each technique has odd arrangements and justifications. Take the class of feature importance methods, for instance. LIME calculates word significance by training a regression model and displaying the user with the learned weights. 

When quantifying word contributions, researchers frequently consider …

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