  Feature attribution methods are exceedingly popular in interpretable machine
learning. They aim to compute the attribution of each input feature to
represent its importance, but there is no consensus on the definition of
"attribution", leading to many competing methods with little systematic
evaluation. The lack of attribution ground truth further complicates
evaluation, which has to rely on proxy metrics. To address this, we propose a
dataset modification procedure such that models trained on the new dataset have
ground truth attribution available. We evaluate three methods: saliency maps,
rationales, and attention. We identify their deficiencies and add a new
perspective to the growing body of evidence questioning their correctness and
reliability in the wild. Our evaluation approach is model-agnostic and can be
used to assess future feature attribution method proposals as well. Code is
available at https://github.com/YilunZhou/feature-attribution-evaluation.
