# How Culture Shapes What People Want From AI

arXiv: https://arxiv.org/abs/2403.05104 · HTML: https://mlsanity.com/paper/2403.05104/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 127 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Does Siri Have a Soul? Exploring Voice Assistants Through Shinto Design Fictions](https://mlsanity.com/paper/2003.03207/) (2020)

## Built on by (most cited first, 23 of 23)

- [ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://mlsanity.com/paper/2505.13995/) (2025)
- [Build the web for agents, not agents for the web](https://mlsanity.com/paper/2506.10953/) (2025)
- [Culture is Everywhere: A Call for Intentionally Cultural Evaluation](https://mlsanity.com/paper/2509.01301/) (2025)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions](https://mlsanity.com/paper/2502.13780/) (2025)
- [Computational Hermeneutics: Evaluating generative AI as a cultural technology](https://mlsanity.com/paper/2604.16403/) (2026)
- [QE4PE: Word-level Quality Estimation for Human Post-Editing](https://mlsanity.com/paper/2503.03044/) (2025)
- [Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement](https://mlsanity.com/paper/2409.13726/) (2024)
- [AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum](https://mlsanity.com/paper/2409.10552/) (2024)
- [Rethinking AI Cultural Alignment](https://mlsanity.com/paper/2501.07751/) (2025)
- [Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages](https://mlsanity.com/paper/2502.09532/) (2025)
- [DaKultur: Evaluating the Cultural Awareness of Language Models for Danish with Native Speakers](https://mlsanity.com/paper/2504.02403/) (2025)
- [Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupations](https://mlsanity.com/paper/2510.21011/) (2025)
- [Generative AI and Power Imbalances in Global Education: Frameworks for Bias Mitigation](https://mlsanity.com/paper/2406.02966/) (2024)
- [Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models](https://mlsanity.com/paper/2407.02067/) (2024)
- [Revisiting Trust in the Era of Generative AI: Factorial Structure and Latent Profiles](https://mlsanity.com/paper/2510.10199/) (2025)
- [Cultural Compass: A Framework for Organizing Societal Norms to Detect Violations in Human-AI Conversations](https://mlsanity.com/paper/2601.07973/) (2026)
- [Policy-Grounded Safety Evaluation of 20 Large Language Models](https://mlsanity.com/paper/2507.14719/) (2025)
- [Cultural Authenticity: Comparing LLM Cultural Representations to Native Human Expectations](https://mlsanity.com/paper/2604.03493/) (2026)
- [The Honorific Effect: Exploring the Impact of Japanese Linguistic Formalities on AI-Generated Physics Explanations](https://mlsanity.com/paper/2407.13787/) (2024)
- [What Do We Mean by 'Pilot Study': Early Findings from a Meta-Review of Pilot Study Reporting at CHI](https://mlsanity.com/paper/2602.13488/) (2026)
- [Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maxims](https://mlsanity.com/paper/2606.02493/) (2026)
- [Gricea: An Open Science Platform for Conversational AI Research](https://mlsanity.com/paper/2609.22039/) (2026)

## Abstract (arXiv)

There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.