  Mitigating social biases typically requires identifying the social groups
associated with each data sample. In this paper, we present DAFair, a novel
approach to address social bias in language models. Unlike traditional methods
that rely on explicit demographic labels, our approach does not require any
such information. Instead, we leverage predefined prototypical demographic
texts and incorporate a regularization term during the fine-tuning process to
mitigate bias in the model's representations. Our empirical results across two
tasks and two models demonstrate the effectiveness of our method compared to
previous approaches that do not rely on labeled data. Moreover, with limited
demographic-annotated data, our approach outperforms common debiasing
approaches.
