“Global is Good, Local is Bad?”: Understanding Brand Bias in LLM s (2024)

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.

## introduction the advent of llms has brought unprecedented capabilities in natural language processing, influencing a myriad of applications from content generation to decision support systems. as these technologies become more integrated into everyday life, addressing their inherent biases becomes crucial. this paper investigates the presence of brand bias in llms, through unfair treatment between global and local brands in llm generations. even before llm incorporation, marketing strategies were found to bias consumer interactions and affect recommendation systems (wan et al., 2020; deldjoo and di noia, 2024; ren et al., 2024). these studies advocate for a fairness-aware framework to balance market representation. llm biases can exacerbate this issue, showing up as skewed product recommendations, unfair pricing, and biased search results, which in turn damage trust. such experiences can make consumers feel misled or treated unfairly. moreover, these biases can hinder competition and innovation by favoring well-known brands over new or niche ones, creating significant entry barriers for small businesses and limiting consumer choices. our research aims to check for biases in this space for popular llms such as gpt-4o and llama-3. we seek to find out if llms favor global brands and high-income countries, which could disadvantage local brands and low-income countries. our findings identify specific biases in modern llms and add to the ongoing discussions on making ai technologies fairer. the contributions of this paper are the following. ## task definitions & dataset creation we consider four different types of brands: shoe brands, clothing brands (excluding shoe brands), beverage brands, and electronics brands. ## conclusion this study reveals that llms exhibit brand biases, particularly favoring global brands over local ones, which could affect consumer behavior and brand perception. this could exacerbate challenges for local brands competing in a global market, inadvertently favoring already dominant global brands and potentially stifling competition and innovation. additionally, socio-economic biases affect llm recommendations between luxury and non-luxury brands, correlating with a country's wealth. finally, we found global brand bias may reverse due to country-of-origin effects, revealing potential complications in broad llm behavior generalizations. though we selected four of the most prevalent and advanced llms currently available, our study was not exhaustive. numerous llm variants exist today and more will likely emerge in the future. also, we only consider four types of brands, but there are numerous types of brands available. in experiment 1, we collected actual brand names exclusively from 15 countries. although these were chosen to represent a diverse array of brands, they might not cover all brand categories or geographic regions comprehensively. in experiment 2, we considered only 30 countries with higher gdp and 30 with lower gdp, which may not fully represent global diversity. additionally, we solely used socioeconomic conditions (gdp per capita) to assess the impact, yet llms may also harbor biases related to other social factors such as skin color, gender, and occupation. it would be beneficial to explore these variables to understand how llms perform under varying conditions. our experiments were conducted exclusively in english. this limitation means the behavior of llms, which are capable of operating in multiple languages, might differ in non-english contexts. mengting wan, jianmo ni, rishabh misra, and julian mcauley. 2020. addressing marketing bias in product recommendations. in proceedings of the 13th international conference on web search and data mining, pages 618-626. warat winit, gary gregory, mark cleveland, and peeter verlegh. 2014. global vs local brands: how home country bias and price differences impact brand evaluations. international marketing review, 31(2):102-128.