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.

研究社会经济偏见在大型语言模型中的存在及其变化程度，使用 SilverSpoon 数据集评估模型对社会经济弱势群体的偏见程度，并分析偏见的性质。

大型语言模型中的社会经济偏见调查

Born With a Silver Spoon? Investigating Socioeconomic Bias in Large  Language Models

The COVID-19 pandemic has accentuated socioeconomic disparities across
various racial and ethnic groups in the United States. While previous studies
have utilized traditional survey methods like the Household Pulse Survey (HPS)
to elucidate these disparities, this paper explores the role of social media
platforms in both highlighting and addressing these challenges. Drawing from
real-time data sourced from Twitter, we analyzed language patterns related to
four major types of adverse experiences: loss of employment income (LI), food
scarcity (FS), housing insecurity (HI), and unmet needs for mental health
services (UM). We first formulate a sparsity optimization problem that extracts
low-level language features from social media data sources. Second, we propose
novel constraints on feature similarity exploiting prior knowledge about the
similarity of the language patterns among the adverse experiences. The proposed
problem is challenging to solve due to the non-convexity objective and
non-smoothness penalties. We develop an algorithm based on the alternating
direction method of multipliers (ADMM) framework to solve the proposed
formulation. Extensive experiments and comparisons to other models on
real-world social media and the detection of adverse experiences justify the
efficacy of our model.

COVID-19 疫情加剧了美国不同种族和民族之间的社会经济差距，本研究探索社交媒体平台在凸显和解决这些挑战中的作用，通过分析 Twitter 上与失业收入损失、食物匮乏、住房不安全和精神健康服务需求不满等四种主要不利经历相关的语言模式，提出了一个基于社交媒体数据源提取语言特征的稀疏优化问题，并通过利用先前关于不良经历语言模式相似性的先验知识提出了新的特征相似性约束，该问题由于非凸目标和不平滑惩罚而具有挑战性，我们基于交替方向乘子法（ADMM）框架开发了一个算法来解决所提出的问题，对真实世界的社交媒体数据进行了广泛的实验和与其他模型的比较，证明了我们模型的有效性。