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We argue that we need to evaluate model interpretability methods 'in thewild', i.e., in situations where professionals make critical decisions, andmodels can potentially assist them. We present an in-the-wild evaluation oftoken attribution based on Deep Taylor Decomposition, with professionaljournalists performing reliability assessments. We find that using this methodin conjunction with RoBERTa-Large, fine-tuned on the Gossip Corpus, led tofaster and better human decision-making, as well as a more critical attitudetoward news sources among the journalists. We present a comparison of human andmodel rationales, as well as a qualitative analysis of the journalists'experiences with machine-in-the-loop decision making.

Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild