Auditing Preferences for Brands and Cultures in LLMs
Large language models (LLMs) based AI systems increasingly mediate what billions of people see, choose and buy. This creates an urgent need to quantify the systemic risks of LLM-driven market intermediation, including its implications for market fairness, competition, and the diversity of information exposure. This paper introduces ChoiceEval, a reproducible framework for auditing preferences for brands and cultures in large language models (LLMs) under realistic usage conditions. ChoiceEval addresses two core technical challenges: (i) generating realistic, persona-diverse evaluation queries and (ii) converting free-form outputs into comparable choice sets and quantitative preference metr...
| Authors | Jasmine Rienecker, Katarina Mpofu, Naman Goel et al. |
|---|---|
| Primary category | cs.HC |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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