arXiv:2602.04306v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing -- differences in how semantically equivalent prompts are expressed (e.g., "A is better than B" vs. "B is worse than A") -- as an underexplored contributor to this gap. We first introdu
As LLMs are deployed in real-world applications, identifying and mitigating hidden biases beyond standard evaluations becomes critical for their trustworthy adoption.
A strategic reader should care because pervasive framing effects in LLMs can lead to biased outcomes in critical applications, eroding trust and impacting decision-making fairness.
The identification of framing as a key contributor to hidden LLM bias highlights the need for new debiasing techniques and more robust evaluation methodologies, shifting focus beyond current fairness metrics.
- · AI ethicists
- · LLM debiasing solution providers
- · Organizations prioritizing fair AI deployment
- · LLM developers ignoring subtle biases
- · Applications relying on unexamined LLM outputs
- · Users susceptible to framing effects
Immediate first-order effect is increased research and development into debiasing techniques for LLMs to address framing effects.
A plausible second-order consequence is the development of industry standards or regulatory guidelines for evaluating and mitigating framing biases in AI systems.
A speculative but reasoned third-order consequence could be a shift in user interaction design with AI, employing neutral phrasing to avoid unintentional influence.
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Read at arXiv cs.CL