Do Feature Attribution Methods Correctly Attribute Features?

TitleDo Feature Attribution Methods Correctly Attribute Features?
Publication TypeConference Paper
Year of Publication2022
AuthorsZhou, Y., S. Booth, M. Tulio Ribeiro, and J. Shah
Conference NameProceedings of the 36th AAAI Conference on Artificial Intelligence
Date Published02/2022
AbstractFeature 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.