  Feature attribution methods are popular in interpretable machine learning.
These methods 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,
complicated in particular by the lack of ground truth attribution. To address
this, we propose a dataset modification procedure to induce such ground truth.
Using this procedure, we evaluate three common methods: saliency maps,
rationales, and attentions. We identify several deficiencies and add new
perspectives to the growing body of evidence questioning the correctness and
reliability of these methods applied on datasets in the wild. We further
discuss possible avenues for remedy and recommend new attribution methods to be
tested against ground truth before deployment. The code is available at
https://github.com/YilunZhou/feature-attribution-evaluation
