检测大模型在招聘中对残障人士的交叉歧视，发现主流模型难以识别此类偏见。
ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios
- 构建针对残障歧视的五项专用指标与三项交叉歧视指标
- 2820个场景中残障候选人遭遇显著歧视，尤其叠加性别与种姓时更严重
- 揭示当前安全工具对交叉性偏见的盲区，适合政策制定与模型评估者参考
大型语言模型在高风险领域如招聘中被广泛质疑存在基于身份的歧视，尤其针对残障人士（PwD）。然而现有研究多集中于西方语境，忽视了全球南方地区性别、种姓等多重边缘化如何影响残障人士的体验。我们对六种主流大模型在2820个涵盖不同残疾类型、性别、国籍和种姓背景的招聘场景中进行了全面审计。为捕捉微妙的交叉性伤害，提出ABLEIST（歧视、励志化、超人化、符号化）框架，包含五项残障相关危害指标与三项交叉性危害指标，依据残疾研究文献建立。结果表明，残障候选人的有害内容显著增加，且许多前沿模型未能识别这些偏见；尤其在性别与种姓边缘化的残障人士中，符号化等交叉性危害急剧上升，暴露出当前安全工具的重大盲点，亟需在高风险领域引入交叉性安全评估。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hiring, particularly against people with disabilities (PwD). However, existing research remains largely Western-centric, overlooking how intersecting forms of marginalization--such as gender and caste--shape experiences of PwD in the Global South. We conduct a comprehensive audit of six LLMs across 2,820 hiring scenarios spanning diverse disability, gender, nationality, and caste profiles. To capture subtle intersectional harms and biases, we introduce ABLEIST (Ableism, Inspiration, Superhumanization, and Tokenism), a set of five ableism-specific and three intersectional harm metrics grounded in disability studies literature. Our results reveal significant increases in ABLEIST harms towards disabled candidates--harms that many state-of-the-art models failed to detect. These harms were further amplified by sharp increases in intersectional harms (e.g., Tokenism) for gender and caste-marginalized disabled candidates, highlighting critical blind spots in current safety tools and the need for intersectional safety evaluations of frontier models in high-stakes domains like hiring.
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