AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances (2409.11360v3)

Abstract: 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.

• The paper shows that AI suggestions yield productivity gains mainly for Western users while diminishing cultural nuances in non-Western writing.
• The experiment with Indian and U.S. participants highlights significant differences in AI engagement and stylistic adaptation.
• The study calls for culturally-conscious AI designs to preserve diverse cultural expressions and counter digital neocolonialism.

On the Cultural Homogenization of Writing Induced by Western-Centric AI Models

The paper "AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances" presents an empirical investigation aimed at understanding the cultural impacts of AI-generated suggestions. The researchers, Dhruv Agarwal and Aditya Vashistha from Cornell University, examine the effects of AI integrations, particularly those with a Western-centric bias, on writing styles across cultures. Their paper is based on an experiment involving 118 participants from India and the United States, who engaged in culturally grounded writing tasks with and without AI assistance. The research reveals that the AI suggestions lead to productivity gains that favor Western users and distort cultural expression by aligning non-Western users' writing with Western norms.

Summary of Findings

The paper meticulously addresses two primary research questions. Firstly, it explores whether Western-centric AI models offer more substantial benefits to users from Western cultures compared to non-Western cultures. Secondly, it investigates whether these models harmonize the writing styles of non-Western cultures towards Western styles.

The experimental setup utilized a cross-cultural design involving participants from India and the United States. These participants were tasked with writing exercises derived from Hofstede’s Cultural Onion framework, allowing the researchers to explore both explicit and implicit cultural elements. Participants were divided into four groups based on cultural background and AI usage, and their essays were analyzed for AI reliance, suggestion acceptance rates, task completion times, and lexical diversity among other metrics.

Crucially, the paper discovers measurable differences in AI engagement and productivity benefits between the cultural cohorts. While AI suggestions increased productivity across the board, American participants derived more net value per suggestion. Meanwhile, Indian participants predominantly altered their writing to align with Western styles when aided by AI—revealing an implicit form of cultural imperialism. This shift suggests that AI can subtly erode distinct cultural identifiers in non-Western writing styles, a concern for cultural diversity preservation.

Implications

The paper’s conclusions call attention to the broader implications of AI-driven cultural changes. While AI systems promise productivity enhancements, their Western-centric biases pose risks of cultural homogenization and may contribute to a form of digital neocolonialism. Such systems encourage the erosion of cultural nuances by subtly aligning user output with Western norms, thereby supporting a model of cultural hegemony predicated on silent yet pervasive Western influence.

Practically, these findings underline the necessity for culturally-conscious design in AI models, where mechanisms need to be incorporated to mitigate the encroachment on non-Western cultural expressions. This might include customization features allowing for cultural context adaption within AI interfaces or deploying region-specific model training to ensure diverse representational value.

Future Directions

Looking forward, AI research will need to deeply consider the entanglement of model biases with cultural hegemony. There's a growing responsibility to account for cultural nuances in AI training data and model behavior, ensuring a global plurality of cultural representations. Further research could expand this work to incorporate more diverse cultural backgrounds beyond the Indian-American dichotomy, as well as investigate additional layers of cultural expression potentially affected by AI interactions.

Overall, this paper provides a significant lens on the cultural ramifications of integrating Western-biased AI tools in a culturally diverse world, calling for thoughtful innovation in generating truly inclusive AI solutions.