arXiv:2511.06148v4 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In this paper, we argue that the predominant approach of simply removing existing biases from models is not enough. Using a paradigm from the psychology literature, we demonstrate that LLMs can spontaneously develop novel social biases about artificial demographic groups even when no inherent differences exist. These biases result in highly stratified task allocat
The increasing integration of LLMs into decision-making frameworks necessitates a deeper understanding of their potential to develop biases beyond their training data, as models become more autonomous.
This research reveals a fundamental ethical and operational challenge for sophisticated readers: LLMs can generate novel, emergent biases without explicit programming, significantly impacting fairness and equity in AI-driven decisions.
The focus shifts from merely removing existing biases in LLMs to actively designing systems that prevent the spontaneous development of new and unpredictable social biases.
- · AI ethics researchers
- · AI governance bodies
- · Developers of bias detection and mitigation tools
- · Organizations deploying unmitigated LLM agents
- · Populations subject to biased AI decisions
- · LLM developers reliant solely on post-hoc bias removal
Increased scrutiny and demand for robust ethical AI frameworks and pre-deployment bias testing for autonomous AI systems.
Development of new AI architectures and training methodologies specifically designed to counter emergent bias formation, potentially increasing computational complexity and development costs.
Growing public distrust in AI systems perceived to be inherently biased, leading to regulatory pushback and a slower societal adoption of AI agents in sensitive domains.
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Read at arXiv cs.AI