LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions
We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' autonomy and well-being, we develop a typology and LLM-based methods to measure this form of AI reliance at scale and understand how people are offloading judgment and decision-making to AI. Applying our typology to public usage data (68K prompts from WildChat and ThoughtTrace), we find that LLM-as-oracle use has increased over time (2023-2026) and is more prevalent among younger users. We further build a privacy-preserving data donation tool to analyze individuals' longitudinal usage data (140K prompts from 52 participants), identifying similar...
| Authors | Myra Cheng, Lujain Ibrahim, Grace Liu et al. |
|---|---|
| Primary category | cs.CY |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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