We study the performance-fairness trade-off in more than a dozen fine-tuned LMs for toxic text classification. We empirically show that no blanket statement can be made with respect to the bias of large versus regular versus compressed models. Moreover, we find that focusing on fairness-agnostic performance metrics can lead to models with varied fairness characteristics.

翻译：我们用十几个微调的LMs对毒性文本分类进行了绩效公平权衡研究。 我们的经验显示,对于大型模型与常规模型和压缩模型的偏向性,不能做出一概而论的陈述。 此外,我们发现,侧重于公平、不可知性绩效衡量标准可以导致具有不同公平性特点的模式。