Intersectional Bias in J apanese Large Language Models from a Contextualized Perspective (2025)

Abstract: An growing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality—the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes.

## introduction large language models (llms) can learn unexpected biases during training, and the reproduction of such biases in downstream tasks has become an issue @xcite . various benchmarks such as bbq (bias benchmark for qa) @xcite have been provided to evaluate social bias (i.e., unfair judgments, beliefs, and attitudes toward groups or individuals based on their social attributes such as gender) in llms @xcite . however, most of these benchmarks have focused on bias toward one single social attribute. echoing research highlighting the importance of assessing intersectional bias @xcite , we apply the framework of intersectionality in our benchmark to further explore the complicity of social bias in llms. note that intersectionality focuses on the fact that prejudice-based discrimination and violence do not arise from the effects of a single social attribute in isolation from other social attributes, but rather from various social attributes that are mutually constitutive and contextualized in a particular social condition (collins and bilge, 2020). to address this nuanced sense of bias in its identification, we follow the concept of microaggressions as developed by the psychologist d. w. sue (sue and spanierman, 2020). microaggressions are not macro-level discourses such as policies but rather micro-level interactions that occur in everyday practices. they encompass not only verbal or conscious behaviors but also unconscious behaviors or attitudes that communicate invalidation or derogatory treatment of particular individuals. in this study, we created the japanese benchmark inter-jbbq, which evaluates intersectional bias and its detrimental effects in llms on the questionanswering (qa) setting related to microaggressions appearing in social contexts specific to japan. we present an initial test of inter-jbbq as a benchmark by using it to analyze japanese llms, and the results provide insights for further developing the inter-jbbq dataset for public release in order to examine intersectional bias from a contextualized perspective. intersectional bias in llms previous studies have analyzed intersectional bias in llms. @xcite analyzed which occupations the model tended to output for attributes that crossed gender, religion, and sexual orientation. @xcite ambiguous context +disambiguated context negative question non-neg question a and b participated in a matchmaking event. a is an american woman in her 20s earning 5 million yen annually, whereas b is an american man in his 20s earning 5 million yen annually. one paid a 5,000 yen participation fee, and the other paid 20,000 yen. a found a marriage partner, but b was fooling around and ended up breaking a glass at the venue, which he had to compensate for. who paid the 20,000 yen participation fee, a or b? who paid the 5,000 yen participation fee, a or b? topic context category social attribute house renting contract issue, security deposit nationality, gender, race, employment status, education marriage market event participation fee, matching rate gender, age, salary, nationality, occupation research phd (sciences), phd (humanities) nationality, gender, race, sexual orientation, salary, age social etiquette noise, ignoring greetings nationality, salary, educational background ## discussion to analyze the patterns of bias inherent in the model, it is essential to qualitatively examine the predictions made by each swallow model for each question. to this end, we compared the responses of models with basic prompts to ambiguous questions involving profiles with varying gender categories while controlling for other social attributes. when a model chooses between a or b despite insufficient information for judgment, its response is influenced by stereotypes associated with specific attributes, thereby revealing significant biases. appendix e gives the full set of responses used for analysis. a comparison of two topics ( additionally, the analysis highlights the presence of harmful biases. while in the topic of research: phd (humanities), there is no consistent tendency to select female over the contrast category (male), in the topic of marriage market: matching rate, the model consistently predicts female over the contrast categories (male and transgender). this result can be interpreted as reflecting the pronounced commodification of the female gender in marriagerelated activities. ## conclusion we created inter-jbbq to evaluate intersectional bias in llms from a contextualized perspective. experiments with swallow and gpt-4o revealed that the accuracy changed according to the attribute combination. detailed analysis with our intersectional framework indicated that social biases by llms on the same social attributes can vary depending on the contexts. in future work, we will consider methods for creating our dataset more efficiently while maintaining quality, such as automating the filtering of unnatural profile sentences and creating templates from existing sources or with the assistance of llms. in addition, we will improve our analysis method and continue to analyze intersectional bias in llms.