Social science research has shown that candidates with names indicative of
certain races or genders often face discrimination in employment practices.
Similarly, Large Language Models (LLMs) have demonstrated racial and gender
biases in various applications. In this study, we utilize GPT-3.5-Turbo and
Llama 3-70B-Instruct to simulate hiring decisions and salary recommendations
for candidates with 320 first names that strongly signal their race and gender,
across over 750,000 prompts. Our empirical results indicate a preference among
these models for hiring candidates with White female-sounding names over other
demographic groups across 40 occupations. Additionally, even among candidates
with identical qualifications, salary recommendations vary by as much as 5%
between different subgroups. A comparison with real-world labor data reveals
inconsistent alignment with U.S. labor market characteristics, underscoring the
necessity of risk investigation of LLM-powered systems.

社会科学研究表明，具有表明特定种族或性别的姓名的候选人在就业实践中经常面临歧视。 similarly, 大型语言模型 (LLMs) 在各种应用中展示了种族和性别偏见。在本研究中，我们利用 GPT-3.5-Turbo 和 Llama 3-70B-Instruct 来模拟对具有 320 个姓氏强烈表示其种族和性别的候选人的招聘决策和薪水建议，覆盖了超过 750,000 个提示。我们的实证结果表明，这些模型偏好招聘白人女性名字听起来的候选人，并选择其他人口群体中的 40 个职业。此外，即使在具有相同资格的候选人中，薪水建议在不同子群体之间也会有高达 5% 的差异。与现实劳动力数据的比较显示，与美国劳动力市场特征不一致，凸显了对由 LLMs 驱动的系统的风险调查的必要性。

你得是医生，林”：对大型语言模型在就业推荐中基于姓名的偏见的研究

"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of  Large Language Models in Employment Recommendations

Models that are learned from real-world data are often biased because the
data used to train them is biased. This can propagate systemic human biases
that exist and ultimately lead to inequitable treatment of people, especially
minorities. To characterize bias in learned classifiers, existing approaches
rely on human oracles labeling real-world examples to identify the "blind
spots" of the classifiers; these are ultimately limited due to the human labor
required and the finite nature of existing image examples. We propose a
simulation-based approach for interrogating classifiers using generative
adversarial models in a systematic manner. We incorporate a progressive
conditional generative model for synthesizing photo-realistic facial images and
Bayesian Optimization for an efficient interrogation of independent facial
image classification systems. We show how this approach can be used to
efficiently characterize racial and gender biases in commercial systems.

使用逐步条件生成模型和贝叶斯优化的方法，通过生成对抗网络来分析分类器在种族和性别方面的偏差。