The societal impact of pre-trained language models has prompted researchers
to probe them for strong associations between protected attributes and
value-loaded terms, from slur to prestigious job titles. Such work is said to
probe models for bias or fairness-or such probes 'into representational biases'
are said to be 'motivated by fairness'-suggesting an intimate connection
between bias and fairness. We provide conceptual clarity by distinguishing
between association biases (Caliskan et al., 2022) and empirical fairness (Shen
et al., 2022) and show the two can be independent. Our main contribution,
however, is showing why this should not come as a surprise. To this end, we
first provide a thought experiment, showing how association bias and empirical
fairness can be completely orthogonal. Next, we provide empirical evidence that
there is no correlation between bias metrics and fairness metrics across the
most widely used language models. Finally, we survey the sociological and
psychological literature and show how this literature provides ample support
for expecting these metrics to be uncorrelated.

该论文研究了预先训练的语言模型的偏见和公平性之间的关系，提出了关联偏见和实证公平性的区别，并通过实证结果展示二者之间可能独立存在，最后指出现有的社会学和心理学文献支持这些度量是不相关的。