Socioeconomic bias in society exacerbates disparities, influencing access to opportunities and resources based on individuals' economic and social backgrounds. This pervasive issue perpetuates systemic inequalities, hindering the pursuit of inclusive progress as a society. In this paper, we investigate the presence of socioeconomic bias, if any, in large language models. To this end, we introduce a novel dataset SilverSpoon, consisting of 3000 samples that illustrate hypothetical scenarios that involve underprivileged people performing ethically ambiguous actions due to their circumstances, and ask whether the action is ethically justified. Further, this dataset has a dual-labeling scheme and has been annotated by people belonging to both ends of the socioeconomic spectrum. Using SilverSpoon, we evaluate the degree of socioeconomic bias expressed in large language models and the variation of this degree as a function of model size. We also perform qualitative analysis to analyze the nature of this bias. Our analysis reveals that while humans disagree on which situations require empathy toward the underprivileged, most large language models are unable to empathize with the socioeconomically underprivileged regardless of the situation. To foster further research in this domain, we make SilverSpoon and our evaluation harness publicly available.

翻译：社会经济偏见加剧了社会不平等，基于个人经济和社会背景影响其获取机会与资源的可能性。这一普遍存在的问题使系统性不平等长期存在，阻碍了社会实现包容性进步的追求。本文旨在探究大型语言模型中是否存在社会经济偏见。为此，我们提出了一个新颖的数据集SilverSpoon，该数据集包含3000个样本，这些样本描述了假设情境：处境不利者因自身境况而做出伦理模糊的行为，并询问该行为是否在伦理上具有正当性。此外，该数据集采用双重标注机制，并由社会经济光谱两端的人群共同完成标注。利用SilverSpoon，我们评估了大型语言模型所表现出的社会经济偏见程度，以及该程度随模型规模变化的趋势。我们还进行了定性分析以探究此类偏见的本质。分析结果表明：尽管人类对于何种情境需要对弱势群体给予共情存在分歧，但大多数大型语言模型无论情境如何均无法对经济社会处境不利者产生共情。为促进该领域的进一步研究，我们将公开SilverSpoon数据集及评估框架。