Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model (2023)

Abstract: Existing bias mitigation methods require social-group-specific word pairs (e.g., “man” – “woman”) for each social attribute (e.g., gender), restricting the bias mitigation to only one specified social attribute. Further, this constraint renders such methods impractical and costly for mitigating bias in understudied and/or unmarked social groups. We propose that the Stereotype Content Model (SCM) — a theoretical framework developed in social psychology for understanding the content of stereotyping — can help debiasing efforts to become social-group-agnostic by capturing the underlying connection between bias and stereotypes. SCM proposes that the content of stereotypes map to two psychological dimensions of warmth and competence. Using only pairs of terms for these two dimensions (e.g., warmth: “genuine” – “fake”; competence: “smart” – “stupid”), we perform debiasing with established methods on both pre-trained word embeddings and large language models. We demonstrate that our social-group-agnostic, SCM-based debiasing technique performs comparably to group-specific debiasing on multiple bias benchmarks, but has theoretical and practical advantages over existing approaches.

## introduction the societal impacts of natural language processing (nlp) have stimulated research on measuring and mitigating the unintended social-group biases encoded in language models @xcite . however, the majority of this important line of work is atheoretical in nature and "fails to engage critically with what constitutes 'bias' in the first place" @xcite . the bias found in language models is rooted in human biases @xcite ; thus, to alleviate such biases, we should ground our debiasing approaches in social psychological theories of stereotyping. these theories can help us shed light on the group-specific group agnostic (our approach) debiasing goal group specific terms gender men,woman race white,black,asian gender race religion age gender race religion age alg. alg. gender race religion age alg. scm terms genuine,fake smart,stupid warmth competence competence jewish middle class rich warmth women christians whites gay men blacks muslims hispanics men asians jewish middle class rich warmth competence women christians whites gay men blacks muslims hispanics men asians w c w w c c mitigated bias general debiasing figure 1 : group-specific debiasing vs. our proposed group-agnostic approach. rather than iteratively debiasing with respect to each social attribute (e.g., gender or race), embeddings or language models are debiased with respect to warmth and competence, the two dimensions of the stereotype content model (scm). although there is a multitude of approaches to bias mitigation @xcite @xcite @xcite , most of these approaches are group-specific. such methods, which debias along subspaces defined by social groups or attributes (e.g., gender or race), are not only atheoretical but also unscalable. resources developed for bias mitigation on one social group or attribute (e.g., gender) do not axiomatically translate easily into other groups or attributes (e.g., age). for example, previous works' focus on gender bias has driven the development of resources that are only applicable to gender debiasing (e.g., equality word sets for gender), but biases associated with other social groups and/or attributes remain understudied. beyond the challenge of creating such resources for a given attribute, to achieve an "unbiased" model with group-specific debiasing, one would have to iterate over all social groups. this approach is practically impossible and arguably would result in significant degrading in the expressiveness of the model. furthermore, groupspecific debiasing is limited in terms of effectiveness: stereotypic relations in distributed representations are deep-rooted, and thus may not be easily removed using explicit sets of group-specific words @xcite . in contrast, a social-group-agnostic approach would not have such restrictions. the stereotype content model (scm; @xcite is a theoretical framework developed in social psychology to understand the content and function of stereotypes in interpersonal and intergroup interactions. the scm proposes that human stereotypes are captured by two primary dimensions of social perception: warmth (e.g., trustworthiness, friendliness) and competence (e.g., capability, assertiveness). from a socio-functional, pragmatic perspective, people's perception of others' intent (i.e., warmth) and capability to act upon their intentions (i.e., competence) affect their subsequent emotion and behavior @xcite . warmth divides people (or groups of people under a social identity) into "friends" or "foes," while competence contains perceptions of social groups' status. depending on historical processes, various social groups may be located in different stereotypic quadrants (high vs. low on warmth and competence) based on this two-dimensional model @xcite . while there are alternative theories related to social evaluation and stereotyping (e.g., @xcite @xcite ellemers et al. (2020) demonstrate the possibility of establishing a theoretical alignment between them. in this work, we propose scm-based bias mitigation and demonstrate that by relying on a theoretical understanding of social stereotypes to define the bias subspace (rather than group-specific subspaces), bias in pre-trained word embeddings and large language models can be adequately mitigated across multiple social attributes (figure 1 ). we chose to focus on scm not only because of the availability of validated linguistic resources @xcite , but also due to scm's prominence, parsimony, and robustness across languages and cultures (e.g., @xcite @xcite . specifically, we confirm that by debiasing with respect to the subspace defined by warmth and competence, our scm-based approach performs comparably with group-specific debiasing for a given group (e.g., scm-based debiasing is comparable to race-debiasing on race, see §3.5). we also show that scm-based debiasing simultaneously reduces bias for understudied attributes such as religion and age ( §3.5 and §4.4). finally, we show that for both word embeddings and large language models scm-based debiasing retains original model's expressiveness ( §3.6 and §4.4). overall, our results confirm the viability of a theory-based, social-group-agnostic approach to bias mitigation foot_0 . ## scm-based bias mitigation for static word embeddings before we discuss our proposed method, we briefly review the algorithms and benchmarks on bias mitigation in word embeddings. there are two components to each post hoc bias mitigation approach for static word embeddings: the bias subspace, which determines the subspace over which the algorithms operate, and the algorithm, which is how the word embeddings are modified with respect to the bias subspace. in this section, we review the concept of bias subspaces, established algorithms for debiasing, and how bias is quantified in word embeddings. finally, we introduce our socialgroup-agnostic framework; scm-based debiasing. ## scm-based bias mitigation for contextualize language models similar to the previous section, before discussing our proposed method, we briefly review bias mitigation efforts in language models. ## conclusion in this work, we demonstrated the viability of a theory-driven approach to debiasing pre-trained word embeddings and language models. by replacing the ad hoc, social-group-specific component of existing debiasing techniques with a general, theory-driven, social-group-agnostic counterpart, we have solved two concrete problems with prior debiasing work and opened the door for more research into theory-driven approaches. first, scmbased debiasing was shown to sufficiently reduce bias across social attributes, without relying on any manually constructed group-specific resources. second, it is evident from our results that scmbased debiasing is scalable with respect to generic social attributes -and embeddings or lms debiased with respect to scm can be thought of as generally debiased. importantly, scm-based debiasing results in improvements on the quality of the respective word embeddings or language models.