---
title: "“Yuki Gets Sushi, David Gets Steak?” — paper by Wei et al. (2026)"
description: "The authors built USChainMains, derived from the 2022 MenuStat release. 52 references."
authors: ["Xuefeng Wei", "Xuan Zhou", "Yusuke Sakai", "Taro Watanabe"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.364/"
doi: "10.18653/v1/2026.eacl-long.364"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wei-2026-yuki-gets-sushi-david-gets/"
---
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# “Yuki Gets Sushi, David Gets Steak?”: Uncovering Gender and Racial Biases in LLM-Based Meal Recommendations

**Authors:** Xuefeng Wei, [Xuan Zhou](https://md.research.elamur.ai/free/en/authors/xuan-zhou/), [Yusuke Sakai](https://md.research.elamur.ai/free/en/authors/yusuke-sakai/) and [Taro Watanabe](https://md.research.elamur.ai/free/en/authors/taro-watanabe/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.364/) · [PDF](https://aclanthology.org/2026.eacl-long.364.pdf) · [DOI 10.18653/v1/2026.eacl-long.364](https://doi.org/10.18653/v1/2026.eacl-long.364) · [OpenAlex](https://openalex.org/W7140118387)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks whether LLMs acting as dietary assistants steer people toward healthier or less healthy food depending only on a name that signals race or gender. The authors built USChainMains, derived from the 2022 MenuStat release. They filtered it to single-serving main courses, removed outliers, and used calorie-stratified sampling to keep ten dishes per restaurant. Eight LLMs were cast as restaurant managers and asked to pick one dish from a fixed menu for customers named with 320 race- and gender-indicative first names. Each pick was scored for compliance with USDA calorie limits and AHA nutrient thresholds. The models more often suggested meals heavy in unhealthy nutrients to names linked with Black, Hispanic, or male users, and Black and Hispanic profiles clustered together as a higher-risk group. Larger models gave healthier picks overall, but the gap between groups remained. Added reasoning reduced gender bias but not racial bias. The work shows a concrete health-equity risk and argues for debiasing methods that take group differences into account.

## Key points
- **USChainMains:** 780 entrées spanning 78 U.S. restaurant chains, all accompanied by standardized nutrition data. It was drawn from MenuStat (26,238 items, 93 chains) and will be released under CC-BY-SA 4.0.
- **Name probes:** 320 names from Nghiem et al. (2024), covering White, Black, Hispanic, and Asian groups and binary gender. The tested LLMs inferred race with 0.93–0.99 accuracy and gender with 0.95–0.99 accuracy.
- **Compliance criteria:** USDA counts a meal as compliant at ≤600 kcal. AHA thresholds are checked per attribute across eight nutrients, such as sodium >960 mg and saturated fat >5 g.
- **Menu baseline:** many dishes already exceed the thresholds (for example, 68.46% for sodium and 72.56% for saturated fat). Because every group sees the identical menu, the gaps come from the models' choices, not the menu itself.
- **Stated limitations:** the study covers only the U.S. and chain restaurants, analyses race and gender separately rather than intersectionally, uses single-turn name-only prompts, and does not test prompts with explicit health goals.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Nara Institute of Science and Technology](https://md.research.elamur.ai/free/en/universities/nara-institute-of-science-and-technology/) — University, Ikoma, Japan · 46 works · 4,565 citations

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## Cite

Cite as: Xuefeng Wei, Xuan Zhou, Yusuke Sakai and Taro Watanabe. 2026. “Yuki Gets Sushi, David Gets Steak?”: Uncovering Gender and Racial Biases in LLM-Based Meal Recommendations. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wei2026yuki,
  title = {{“Yuki Gets Sushi, David Gets Steak?”: Uncovering Gender and Racial Biases in LLM-Based Meal Recommendations}},
  author = {Xuefeng Wei, Xuan Zhou, Yusuke Sakai and Taro Watanabe},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.364},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.364}
}
```

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*Updated 1 November 2026*