arXiv:2606.12443v1 Announce Type: cross Abstract: Social roles shape expectations, priorities, and judgments, yet it remains unclear how large language models (LLMs) associate occupational identities with broader cultural value patterns. Prior work used nationality-based cultural prompting to study how LLM responses to value-survey questions align with human cultural benchmarks. In this paper, we extend that framework by replacing cultural prompting with occupational prompting to examine how professional-role cues influence value-survey responses in open-weight LLMs. Using a survey-grounded ev
The rapid deployment and increasing integration of large language models across various sectors necessitate immediate scrutiny of their inherent biases as they begin to influence real-world outcomes.
Understanding and mitigating cultural biases embedded in LLMs is critical for their fair and ethical deployment, particularly as they assume more decision-making and informational roles that could perpetuate or amplify societal inequalities.
This research shifts the focus from nationality-based cultural bias to occupational bias in LLMs, providing a new lens through which to understand how professional roles are perceived and represented by AI.
- · AI ethics researchers
- · Organizations developing bias detection tools
- · Consultants specializing in AI fairness
- · Developers of unmitigated large language models
- · Sectors relying on biased LLM outputs
- · Users unaware of LLM biases
The study will lead to increased awareness of how occupational stereotypes are embedded in and reinforced by LLMs.
This awareness will drive demand for new methodologies and datasets to de-bias LLMs based on professional identities.
Future regulations may mandate bias testing, including occupational bias, before LLMs can be widely deployed in sensitive applications like hiring or advising.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL