mlsanityData to 7 Oct 2026

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

arXiv 2406.13997 ↗First posted 20 Jun 2024cs.CL cs.CEcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

6 of 18 references.

References · 6

  1. 2023A Survey on Popularity Bias in Recommender Systemscited by 42
  2. 2023The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendationscited by 41
  3. 2023Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Modelscited by 32
  4. 2019Addressing Marketing Bias in Product Recommendationscited by 23
  5. 2024CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender Systemcited by 18
  6. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 7.

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  1. 2025PAARS: Persona Aligned Agentic Retail Shopperscited by 14
  2. 2025Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Modelscited by 7
  3. 2026Auditing Preferences for Brands and Cultures in LLMscited by 4
  4. 2024BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Banglacited by 3
  5. 2025Understanding Gender Bias in AI-Generated Product Descriptionscited by 2
  6. 2025From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Textcited by 2
  7. 2026What Gets Cited: Competitive GEO in AI Answer Enginescited by 2
  8. 2026Investigating Associational Biases in Inter-Model Communication of Large Generative Modelscited by 1
  9. 2026Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systemscited by 1
  10. 2026The Language of the Question Selects the Market: Query Language and Exit IP as Separable Factors in Commercial Recommendations from a Generative Search Interfacecited by 1
  11. 2025CIDER: A Causal Cure for Brand-Obsessed Text-to-Image Modelscited by 0
  12. 2026PriceBench: A Diagnostic Benchmark for Price, Quality, and Brand Preferences in LLM Booking Agentscited by 0
1 more
  1. 2026System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfercited by 0