arXiv:2606.02776v1 Announce Type: new Abstract: When large language models (LLMs) are used in high-stakes scenarios, such as legal, medical and financial advice, even a single conversation history is enough to drive differences in outcomes between users. Prior work has demonstrated that this results in outcome disparities between sociodemographic groups, with some groups receiving more advantageous outcomes than others. In this work, we demonstrate that LLMs actually struggle to infer user sociodemographics from a single conversation history and that although there are disparities between soci
The proliferation of LLMs into high-stakes sectors necessitates a deeper understanding of their biases and differential outcomes based on user interactions.
Understanding how conversational context affects LLM answers and the difficulty in inferring sociodemographics is crucial for fair and equitable deployment of AI, particularly in sensitive applications.
The focus potentially shifts from solely addressing explicit demographic biases to understanding biases introduced or exacerbated by conversational interaction dynamics and the LLM's 'inference' capabilities.
- · AI ethicists and researchers
- · Developers of bias detection and mitigation tools
- · Regulatory bodies
- · LLM developers deploying untested models in high-stakes environments
- · Users experiencing disparate outcomes due to interaction quirks
Increased scrutiny on conversational context in LLM performance and fairness assessments.
Development of new methodologies and metrics to evaluate and mitigate interaction-based disparities in AI outcomes.
Potential for regulations mandating transparency or auditability of AI systems' conversational processing to ensure equitable user experiences.
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Read at arXiv cs.CL