# AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances

arXiv: https://arxiv.org/abs/2409.11360 · HTML: https://mlsanity.com/paper/2409.11360/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 99 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 30 of 38)

- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [Measuring Bias in Contextualized Word Representations](https://mlsanity.com/paper/1906.07337/) (2019)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Towards Measuring and Modeling "Culture" in LLMs: A Survey](https://mlsanity.com/paper/2403.15412/) (2024)
- [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021)
- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models](https://mlsanity.com/paper/2102.04130/) (2021)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [The Ghost in the Machine has an American accent: value conflict in GPT-3](https://mlsanity.com/paper/2203.07785/) (2022)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Gmail Smart Compose: Real-Time Assisted Writing](https://mlsanity.com/paper/1906.00080/) (2019)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [Smart Reply: Automated Response Suggestion for Email](https://mlsanity.com/paper/1606.04870/) (2016)
- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models](https://mlsanity.com/paper/2402.11723/) (2024)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://mlsanity.com/paper/2404.12464/) (2024)

## Built on by (most cited first, 30 of 60)

- [NoveltyBench: Evaluating Language Models for Humanlike Diversity](https://mlsanity.com/paper/2504.05228/) (2025)
- [Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluations](https://mlsanity.com/paper/2601.17087/) (2026)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models](https://mlsanity.com/paper/2502.11266/) (2025)
- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [Taxonomy of User Needs and Actions](https://mlsanity.com/paper/2510.06124/) (2025)
- [Fluent but Foreign: Even Regional LLMs Lack Cultural Alignment](https://mlsanity.com/paper/2505.21548/) (2025)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness](https://mlsanity.com/paper/2502.09637/) (2025)
- [AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini](https://mlsanity.com/paper/2507.22445/) (2025)
- [Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays](https://mlsanity.com/paper/2503.20062/) (2025)
- [The Impact of AI-Generated Text on the Internet](https://mlsanity.com/paper/2604.26965/) (2026)
- [KAHANI: Culturally-Nuanced Visual Storytelling Tool for Non-Western Cultures](https://mlsanity.com/paper/2410.19419/) (2024)
- [Phraselette: A Poet's Procedural Palette](https://mlsanity.com/paper/2503.06335/) (2025)
- [Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agents](https://mlsanity.com/paper/2507.05820/) (2025)
- [Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit](https://mlsanity.com/paper/2510.07226/) (2025)
- [Reactive Writers: How Co-Writing with AI Changes How We Engage with Ideas](https://mlsanity.com/paper/2603.10374/) (2026)
- [Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity](https://mlsanity.com/paper/2504.14125/) (2025)
- [AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations](https://mlsanity.com/paper/2601.12727/) (2026)
- [When Stereotypes GTG: The Impact of Predictive Text Suggestions on Gender Bias in Human-AI Co-Writing](https://mlsanity.com/paper/2409.20390/) (2024)
- [Do Chinese models speak Chinese languages?](https://mlsanity.com/paper/2504.00289/) (2025)
- [Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities](https://mlsanity.com/paper/2604.22749/) (2026)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026)
- [The Culture Funnel: You Can't Align What isn't in the Data](https://mlsanity.com/paper/2606.13808/) (2026)
- [Imagining Design Workflows in Agentic AI Futures](https://mlsanity.com/paper/2509.20731/) (2025)
- [BengaliMoralBench: A Benchmark for Auditing Moral Reasoning in Large Language Models within Bengali Language and Culture](https://mlsanity.com/paper/2511.03180/) (2025)
- [Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publications](https://mlsanity.com/paper/2511.11687/) (2025)
- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- [Incentives shape how humans co-create with generative AI](https://mlsanity.com/paper/2604.03529/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are being increasingly integrated into everyday products and services, such as coding tools and writing assistants. As these embedded AI applications are deployed globally, there is a growing concern that the AI models underlying these applications prioritize Western values. This paper investigates what happens when a Western-centric AI model provides writing suggestions to users from a different cultural background. We conducted a cross-cultural controlled experiment with 118 participants from India and the United States who completed culturally grounded writing tasks with and without AI suggestions. Our analysis reveals that AI provided greater efficiency gains for Americans compared to Indians. Moreover, AI suggestions led Indian participants to adopt Western writing styles, altering not just what is written but also how it is written. These findings show that Western-centric AI models homogenize writing toward Western norms, diminishing nuances that differentiate cultural expression.