arXiv:2602.10117v5 Announce Type: replace Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We call these unverbalized biases. Monitoring models via their stated reasoning is therefore unreliable, and existing bias evaluations typically require predefined categories and hand-crafted datasets. In this work, we introduce a fully automated, black-box pipeline for detecting task-specific unverbalized biases. Given a task dataset, the pipeline uses LLM autoraters to generate candidate bias concepts. It t
The increasing deployment and reliance on Large Language Models for complex tasks necessitate robust methods for identifying and mitigating their inherent biases, especially those not explicitly verbalized.
Sophisticated readers should care because this research addresses a critical limitation of AI systems, enabling more reliable and trustworthy AI deployment in sensitive applications by detecting hidden biases.
The ability to automatically detect 'unverbalized biases' in black-box LLMs provides a new layer of oversight, shifting from manual, predefined bias evaluations to a more dynamic and comprehensive approach.
- · AI developers
- · Organizations deploying LLMs
- · AI ethics researchers
- · Regulators
- · LLM developers ignoring bias detection
- · Manual bias evaluation methodologies
Improved fairness and accuracy in AI-driven decision-making processes.
Increased public and institutional trust in AI systems due to enhanced transparency and reliability regarding bias.
New standards and regulations emerging for AI bias detection and mitigation, influencing LLM development and deployment universally.
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