@inproceedings{govindarajan-etal-2023-counterfactual,
    title = "Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias",
    author = "Govindarajan, Venkata Subrahmanyan  and
      Beaver, David  and
      Mahowald, Kyle  and
      Li, Junyi Jessy",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.813",
    doi = "10.18653/v1/2023.findings-acl.813",
    pages = "12853--12862",
    abstract = "While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts {---} thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship (IGR) labels. Counterfactual probing further reveals that while neural models finetuned for predicting IGR reliably use affect in classification, the model{'}s usage of specificity is inconclusive.",
}

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts {---} thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship (IGR) labels. Counterfactual probing further reveals that while neural models finetuned for predicting IGR reliably use affect in classification, the model{'}s usage of specificity is inconclusive.
Govindarajan, Venkata Subrahmanyan Beaver, David Mahowald, Kyle Li, Junyi Jessy
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias