Contrastive explanations, where one decision is explained in contrast to
another, are supposed to be closer to how humans explain a decision than
non-contrastive explanations, where the decision is not necessarily referenced
to an alternative. This claim has never been empirically validated. We analyze
four English text-classification datasets (SST2, DynaSent, BIOS and
DBpedia-Animals). We fine-tune and extract explanations from three different
models (RoBERTa, GTP-2, and T5), each in three different sizes and apply three
post-hoc explainability methods (LRP, GradientxInput, GradNorm). We furthermore
collect and release human rationale annotations for a subset of 100 samples
from the BIOS dataset for contrastive and non-contrastive settings. A
cross-comparison between model-based rationales and human annotations, both in
contrastive and non-contrastive settings, yields a high agreement between the
two settings for models as well as for humans. Moreover, model-based
explanations computed in both settings align equally well with human
rationales. Thus, we empirically find that humans do not necessarily explain in
a contrastive manner.9 pages, long paper at ACL 2022 proceedings.

对四个英文文本分类数据集进行分析，通过三种不同规模的模型以及三种后期解释性方法，验证了模型解释和人类解释在对比性和非对比性设置下的一致性。

宁愿是护士也不是医生 -- 对比解释的调查研究

Rather a Nurse than a Physician -- Contrastive Explanations under  Investigation

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。