Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are 'right for the right reasons'. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and six demographic groups (balanced across age and ethnicity). Such data enables us to ask both what demographics our predictions align with and whose reasoning patterns our models' rationales align with. We find systematic inter-group annotator disagreement and show how 16 Transformer-based models align better with rationales provided by certain demographic groups: We find that models are biased towards aligning best with older and/or white annotators. We zoom in on the effects of model size and model distillation, finding -- contrary to our expectations -- negative correlations between model size and rationale agreement as well as no evidence that either model size or model distillation improves fairness.

本文采用人类理性注释，跨越情感分析和常识推理三个数据集，并涵盖男女老少不同族裔人群，探究了模型预测与人类理性对齐的程度，并指出了现存的一些主观性问题。作者发现模型存在偏见，更倾向于与年龄较大或白人注释器的理性对齐。

以哪些正确的理由来诉求正确？