The paper "ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios" audits six large language models across 2,820 hiring scenarios that vary by disability, gender, nationality, and caste. It introduces the ABLEIST framework—comprising five ableism-specific and three intersectional harm metrics—to detect subtle biases. The study finds that many state‑of‑the‑art models exhibit significant ABLEIST harms, especially for gender- and caste-marginalized disabled candidates, exposing blind spots in current safety tools.
Text-to-image (T2I) models have been shown to exhibit social biases. Prior work has mainly focused on gender, skin tone, and cultural representation within restricted occupational associations, and emerging benchmarks increasingly incorporate these dimensions.
arXiv:2407.20371v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the...
The study investigates how gender assignments of candidates and evaluators influence decisions made by large language models (LLMs) in a city council voting scenario. Eight LLM-as-a-Judge and decision language models evaluated 240 synthetic professional narratives, producing 30,720 judgments. Results show that explicit candidate gender labels slightly increased the probability of a Yes vote, only nonbinary evaluator assignments had a significant effect, and decision reversals varied across models, with decision language models exhibiting fewer reversals than LLM-as-a-Judge models.
arXiv:2604.20677v3 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and bias, particularly when...
FairLens is a benchmark and evaluation framework that measures fairness and validity of vision‑language models (VLMs) in high‑stakes domains such as hiring, legal, and healthcare. It uses over 100,000 face‑image and question pairs covering gender, race, and age, and assesses responses through demographic parity, soundness, demographic association, and bias in free‑text generation. The study finds that VLMs often make unwarranted inferences from faces rather than abstaining, especially in legal and healthcare contexts, and that small parity gaps can still hide unsafe treatment across groups.
arXiv:2607. 28934v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly involved in the distribution of scarce resources, raising concerns about biased allocations based on characteristics like race and gender.
The paper reports the first systematic audit of open‑weight large language models (LLMs) in hiring contexts, examining how job‑posting language influences recruiter and job‑seeker simulations across six models. It finds that agentic language lowers recruiter scores for female candidates while communal language mitigates this effect, and that coded‑exclusion language sharply reduces recruiter scores for non‑White candidates and discourages non‑White personas from applying. The study also identifies the explicit demographic label as the main causal factor and proposes a concrete pre‑deployment audit protocol aligned with EU and U.S. regulatory requirements.
arXiv:2507. 11548v3 Announce Type: replace-cross Abstract: The use of publicly available generative AI systems for resume evaluation is often justified by the assumption that these tools reduce bias relative to human judgment.
The study examines how the choice of large language model (LLM) used to write job application cover letters influences hiring decisions, independent of the applicant’s qualifications. Using a multi‑model audit with eleven writer agents and nine evaluator agents across ten job descriptions, the authors find a consistent ranking of writer models—locally hosted models are disadvantaged and evaluators often favor their own outputs. The hierarchy has tangible effects, with up to 1.8 of 25 qualified candidates being rejected solely due to model mismatch, and the patterns are explained by shared stylistic cues that both writers and evaluators reward or penalise.
arXiv:2609.22169v1 Announce Type: new Abstract: Employers are increasingly using large language models (LLMs) to automate their hiring process. This paper investigates the risk of monocultural biases...
arXiv:2604. 01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly.
The study audits demographic leakage in German-language resumes generated by large language models. Using ChatGPT, Gemini, and Qwen 3 variants, the authors generate resumes from anonymized profiles, varying only gender- and ethnicity-associated names while keeping qualifications constant. Even after anonymization and gender-neutralization, classifiers can reliably distinguish male- from female-generated resumes, driven by subtle differences in gender-neutral terminology rather than overtly gendered wording; ethnicity-related leakage remains weak.