"Global is Good, Local is Bad?": Understanding Brand Bias in LLMs

TL;DR

This paper investigates biases in large language models towards brands, revealing a tendency to favor global and luxury brands, influenced by societal and country-of-origin biases, which could impact market fairness.

Contribution

It introduces a curated dataset and analysis framework to systematically examine brand bias in LLMs, highlighting the prevalence of global and luxury brand favoritism.

Findings

LLMs favor global brands with positive attributes

LLMs disproportionately recommend luxury gifts in high-income contexts

Country-of-origin effects influence brand preferences in LLM outputs

Abstract

Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different brands, a significant concern given the widespread use of LLMs in affected use cases such as product recommendation and market analysis. Biased models may perpetuate societal inequalities, unfairly favoring established global brands while marginalizing local ones. Using a curated dataset across four brand categories, we probe the behavior of LLMs in this space. We find a consistent pattern of bias in this space -- both in terms of disproportionately associating global brands with positive attributes and disproportionately recommending luxury gifts for individuals in high-income countries. We also find LLMs are subject to country-of-origin effects which may boost local brand preference in LLM outputs in specific contexts.