Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias (2023)

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.

## introduction most work on bias in nlp only considers negative or pejorative language use @xcite @xcite @xcite . while recent work has delved into implicit bias @xcite @xcite , they are still limited as they rely on identifying specific demographic dimensions or an individual's intent. crucially, language production is still taken to be 'unbiased' by default. research in social psychology suggests a different framing of bias that encompasses all language use -we can analyze bias as changes in (language) behavior reflecting shifting social dynamics @xcite . under this view, all the language we produce is biased, with the nature of the bias determined by the social relationships between the speaker and target. inspired by this idea, @xcite proposed a new framing of bias by modeling intergroup relationships (igr, in-group and out-group) in interpersonal english language tweets, potentially capturing more subtle forms of bias. this framing raises a question: which linguistic features vary systematically in different intergroup contexts? the linguistic intergroup bias (lib; @xcite hypothesis offers some clues towards linguistic features that change with shifting intergroup contexts. lib speculates that socially desirable in-group behaviors and socially undesirable out-group behaviors are encoded at a higher level of abstraction. the theory however relies on a restricted definition of abstractness that relies solely on predicates, and an ad-hoc analysis of 'social desirability' that doesn't permit largescale analysis. we can do better by using two welldefined pragmatic features: specificity (li, 2017) is a pragmatic feature of text that measures the level of detail (similar to abstract-concrete axis), while affect is a feature that measures the attitude of a speaker towards their target @xcite in an utterance (analogous to social desirability). specificity and affect are analogous to the lib axes of language variation that are easy to annotate and compute. furthermore, specificity is a more general property than abstractness in the lib -specificity is a property of the whole sentence rather than just the predicate. thus, our study focuses on intergroup bias more generally, rather than the narrow parameterization of the lib. similar to the lib, our formulation of intergroup bias predicts that positive affect in-group utterances and negative affect out-group utterances are encoded with lower specificity (i.e. more generally). tables 1 and 2 compare the predicted language variation between the lib and our formulation. in this work, we perform the first large-scale study of linguistic differences in intergroup bias by analyzing its nature in the corpus of english tweets from @xcite , which makes use of naturally occurring labels for in-group vs. outgroup. this distinguishes us from existing work in lib which mostly relies on artificial responses from participants in studies, rather than natural language use in the wild. to bolster our probing investigation, we also explore it causally: exploiting the quantitative nature of our formulation to study if a neural model finetuned for igr prediction uses pragmatic features such as specificity and affect in its decision-making process through counterfactual probing techniques @xcite . to summarize our findings, we find a modest positive correlation between affect and igr in our data, with a positive causation effect as well -making a tweet's affect more positive makes it more likely to be in-group regardless of its specificity. we find no correlation between specificity and igr in our data. surprisingly, we discover a causal effect of low specificity on igr prediction that is uniform across affect, but none for high specificity. we hypothesize that this could be because of damage to the underlying language model, but we leave further investigation to future work. we release our code and data at github.com/venkatasg/intergroup-probing. ## background intergroup bias the linguistic intergroup bias (lib) theory @xcite tries to explain how stereotypes are transmitted and persist in communication by hypothesizing that socially desirable in-group behaviors and socially undesirable out-group behaviors are encoded at a higher level of abstraction . the lib has been reproduced in various psychological experiments and analyses @xcite ; it has also been used as an indicator for a speaker's prejudicial attitudes @xcite @xcite . ## results & analysis the results for the interventions on affect are presented in figure 2 , while those for specificity are presented in figure 3 . overall, we observe that in both cases, interventions had the same effect on tweets that were annotated with positive affect as they did on tweets with negative affect (and similarly for tweets with high and low specificity)so we only show the percentage of all tweets in the test split classified as in-group. affect as figure 2 shows, pushing model representations towards having more positive affect causes almost all tweets in the test split of our data to be classified as in-group after 32 iterations of inlp. the randomness after 40 iterations of inlp could be attributed to the underlying roberta language model being destroyed, as the lm top-100 accuracy plot in appendix d shows. pushing the model's representations towards negative affect shows the inverse effect as expected, although the nature of the drop appears different. we hypothesize that this is because most of the tweets in our dataset (75.2%) have positive affect. an intervention pushing the representations towards negative affect would be slower and require stronger intervention forces, which is borne out in figure 2 . specificity figure 3 shows that pushing model representations towards being more specific has no effect on model behavior and is indistinguishable from the control; but pushing towards lower specificity has a noticeable effect -interventions after 48 iterations of inlp lead to all the data being predicted as in-group. our hypothesis states that general language is more likely in positive affect in-group contexts; however we find no difference in the model's behavior on positive versus negative affect tweets as reported earlier. overall our findings indicate that while the model does use affect towards making its decision on the interpersonal group relationship prediction task (albeit uniformly across specificity), it doesn't use specificity as we had predicted. the discrepancy between high and low specificity interventions could be because the average specificity of tweets in our training data is 3.49 (σ = 0.54) -meaning that interventions towards lower specificity act in opposition to most of our data in representation space. but these results requires further investigation to understand them better. qualitative error analysis digging into the results further, we wanted to investigate if the interventions function the way we wanted them to. we analyzed the tokens that the model predicts before and after intervention for example (1). firstly, finetuning the model for igr prediction leads to degradation in lm abilities -a vanilla model predicts birthday, anniversary for the masked token in (1), but the finetuned model predicts nonsensical tokens like sworn, opport__ even before any interventions. pushing towards negative affect causes it to predict tokens with negative connotations (killing, ass, opposition), but degrades the underlying lm even further. the specificity interventions are especially hard to interpret due to the semantically and syntactically implausible tokens being selected (opport__, mug__, ask__) (1) @xmath0: #iioa while some of the interventions push the model's predictions to be in the general lexical space desired (which probably explain the affect intervention results), the lack of contextual fit due to lm degradation may explain the inconclusive results, and lack of interaction between affect and specificity. ## conclusion studying bias in language use through an interpersonal lens opens up new questions, such as which linguistic features vary systematically in changing interpersonal contexts. we perform a correlational and causal analysis of two pragmatic features, specificity and affect, on a dataset of interpersonal tweets in english, to establish how they influence intergroup relationship prediction. we find modest correlations between our features and igr labels, while counterfactual probing reveals mixed results. affect influences igr prediction causally but without interacting with specificity, while specificity only influences igr prediction in one direction.