Ask LLM s Directly, “What shapes your bias?”: Measuring Social Bias in Large Language Models (2024)

Abstract: Social bias is shaped by the accumulation of social perceptions towards targets across various demographic identities. To fully understand such social bias in large language models (LLMs), it is essential to consider the composite of social perceptions from diverse perspectives among identities. Previous studies have either evaluated biases in LLMs by indirectly assessing the presence of sentiments towards demographic identities in the generated text or measuring the degree of alignment with given stereotypes. These methods have limitations in directly quantifying social biases at the level of distinct perspectives among identities. In this paper, we aim to investigate how social perceptions from various viewpoints contribute to the development of social bias in LLMs. To this end, we propose a novel strategy to intuitively quantify these social perceptions and suggest metrics that can evaluate the social biases within LLMs by aggregating diverse social perceptions. The experimental results show the quantitative demonstration of the social attitude in LLMs by examining social perception. The analysis we conducted shows that our proposed metrics capture the multi-dimensional aspects of social bias, enabling a fine-grained and comprehensive investigation of bias in LLMs.

## introduction stereotypes shape social perceptions-either positive or negative prejudices and pre-existing judgments about particular groups and the people who belong to them without any objective basis @xcite . for example, while "you are a woman, so you must be weak" exemplifies a negative stereotype, the belief that "because you are a man, you are strong" is seen as a positive stereotype. these stereotypes vary from person to person, influenced by factors such as an individual's social identity and personal beliefs, resulting in a unique set of social perceptions for each person @xcite @xcite . grounded on the psychological insight that social bias arises from the collective social perceptions of various individuals @xcite , in this paper, we define social bias as the aggregate impact of social perceptions, as illustrated in figure 1 . recent findings show that language models (lms), designed to replicate human language and social norms, also manifest real-world biases @xcite . various studies are conducted to measure and quantify the biases inherent in lms @xcite @xcite @xcite . one of the most straightforward approaches to quantifying bias involves using a question-answering (qa) format @xcite @xcite . most of the qa-based eval-uations examined the model's adherence to fixed stereotypes, mainly rooted in english-speaking cultures, by offering choices between stereotypical and anti-stereotypical options. this approach has not fully considered that perceptions of each individual toward a target may be different, depending on their unique viewpoints. the concept of social perception outlined in this paper is similarly advanced by @xcite , who utilized the metric regard to describe the overall favorable or unfavorable view of an entity. some studies have conducted assessments of bias in lms using the metric regard @xcite @xcite @xcite . these studies measured bias indirectly by examining the content of the generated text employing a sentiment analysis tool or classifier. however, such an indirect approach requires additional procedures to deduce bias from the outcomes. thus, there is a clear need for a methodology that allows for a more straightforward quantification of social perception. in this paper, we focus on understanding how social perceptions have been formed in large language models (llms) by varied viewpoints towards distinct targets. in addition, we uncover the contours of social bias inherent in llms by aggregating these social perceptions. to this end, we propose a methodology designed to examine the varied perceptions held by llms by adopting a qa format, enabling us to directly quantify these perceptions without the need for additional measurement steps. in addition, to facilitate the examination of the perception of a target from multiple viewpoints, we employed the persona-assigning approach. after assigning a persona to the llm, we questioned its perspective on a specific target. this allowed us to collect the social perception an llm holds framed as "this {persona} will perceive the {target} in {+/-} manner." we also introduce three novel metrics designed to evaluate social bias in llms by aggregating the collected social perceptions: target bias (tb), bias amount (bamt), and persona bias (pb). tb and bamt provide insights into the bias polarity towards targets and the quantity of such biases, respectively. meanwhile, pb uniquely assesses the variance in social perception based on a demographic identity perceived by llms. through the experiments, we quantitatively demonstrated that llms reflect the variations in social bias depending on varied social perceptions. our proposed metrics allow for a fine-grained and comprehensive analysis of bias in llms through a combined interpretation of these metrics. parallel to research focusing on stereotypical biases, some studies propose metrics for measuring biases from multiple angles, including toxicity @xcite , sentiment @xcite , and regard @xcite . regard focuses on the cumulative effect of positive and negative perceptions towards a target, distinct from stereotypical biases. these studies @xcite @xcite @xcite are somewhat indirect, necessitating additional steps to measure biases through the analysis of toxicity or sentiment in the content of model-generated responses. these approaches may face challenges due to confounding factors from the context or the imperfect performance of toxicity and sentiment classifiers, which could lead to misleading results @xcite . our qa-based approach, by contrast, is designed to compute bias scores directly based on the options selected. ## methodology in this section, we introduce our methodology to directly measure social perceptions in qa format and aggregate the social perceptions to quantify bias. who do language models love, and who do the models think loves whom? ## results and analysis in this section, we present the overall experimental results and provide in-depth analyses. specifically, figure 3 shows the overall results of the bias metrics tb, bamt, pb, and bs. in this figure, our proposed metrics capture multi-dimensional aspects of bias with social perceptions, compared to the bs score, which can capture only a singledimensional aspect. note that we define social bias by the aggregation of social perceptions, where we provide a specific example of the overall social per- ## conclusion in this paper, we described a social bias in llms as the collective impact of social perceptions. leveraging these characteristics, we designed a novel scoring strategy to quantify social perceptions within the qa format. in addition, we introduced three novel metrics-target bias, bias amount, persona bias-specifically developed to capture multifaceted aspects of social bias in llms, by aggregating the social perceptions we measured. through our experiments, we confirmed the successful application of our scoring approach to the bias benchmark dataset formatted for qa. our analysis demonstrates that our approach enables measuring social perceptions of llms and in-depth quantitative analyses such as social attitudes of llms. from these findings, we confirm that measuring the perception and prejudice associated with each identity allows us to delineate the shape of bias, facilitating a deeper analysis of llms' biases.