Counterfactually Augmented Data (CAD) aims to improve out-of-domain
generalizability, an indicator of model robustness. The improvement is credited
with promoting core features of the construct over spurious artifacts that
happen to correlate with it. Yet, over-relying on core features may lead to
unintended model bias. Especially, construct-driven CAD -- perturbations of
core features -- may induce models to ignore the context in which core features
are used. Here, we test models for sexism and hate speech detection on
challenging data: non-hateful and non-sexist usage of identity and gendered
terms. In these hard cases, models trained on CAD, especially construct-driven
CAD, show higher false-positive rates than models trained on the original,
unperturbed data. Using a diverse set of CAD -- construct-driven and
construct-agnostic -- reduces such unintended bias.

研究结论表明：使用 CAD 的模型在面对一些难以处理的数据集时，由于对核心元素进行扰动引入了模型偏见，甚至比原始数据训练的模型误报率更高。使用多样化的 CAD (既有构造驱动又有用于基础特征不变的 CAD) 可以减少此类意外偏差。