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

arXiv: https://arxiv.org/abs/2406.13997 · HTML: https://mlsanity.com/paper/2406.13997/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations](https://mlsanity.com/paper/2308.02053/) (2023)
- [Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models](https://mlsanity.com/paper/2309.08902/) (2023)
- [Addressing Marketing Bias in Product Recommendations](https://mlsanity.com/paper/1912.01799/) (2019)
- [CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System](https://mlsanity.com/paper/2403.05668/) (2024)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)

## Built on by (most cited first, 15 of 15)

- [PAARS: Persona Aligned Agentic Retail Shoppers](https://mlsanity.com/paper/2503.24228/) (2025)
- [Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models](https://mlsanity.com/paper/2504.05325/) (2025)
- [Auditing Preferences for Brands and Cultures in LLMs](https://mlsanity.com/paper/2603.18300/) (2026)
- [BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla](https://mlsanity.com/paper/2409.11638/) (2024)
- [Understanding Gender Bias in AI-Generated Product Descriptions](https://mlsanity.com/paper/2506.05390/) (2025)
- [Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems](https://mlsanity.com/paper/2606.17443/) (2026)
- [From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text](https://mlsanity.com/paper/2508.09450/) (2025)
- [What Gets Cited: Competitive GEO in AI Answer Engines](https://mlsanity.com/paper/2605.25517/) (2026)
- [Investigating Associational Biases in Inter-Model Communication of Large Generative Models](https://mlsanity.com/paper/2601.22093/) (2026)
- [The Language of the Question Selects the Market: Query Language and Exit IP as Separable Factors in Commercial Recommendations from a Generative Search Interface](https://mlsanity.com/paper/2608.30052/) (2026)
- [CIDER: A Causal Cure for Brand-Obsessed Text-to-Image Models](https://mlsanity.com/paper/2509.15803/) (2025)
- [PriceBench: A Diagnostic Benchmark for Price, Quality, and Brand Preferences in LLM Booking Agents](https://mlsanity.com/paper/2609.31468/) (2026)
- [System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer](https://mlsanity.com/paper/2610.00253/) (2026)
- [What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)](https://mlsanity.com/paper/2610.15033/) (2026)
- [Twenty Days Later: A Pre-Registered Test of History-Based Forecasts of Brand Mentions in Gemini Answers](https://mlsanity.com/paper/2610.19608/) (2026)

## Abstract (arXiv)

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.