The AI Gap: How Socioeconomic Status Affects Language Technology Interactions

TL;DR

This study investigates how socioeconomic status influences interactions with language technologies and AI, revealing systematic usage differences that may widen the digital divide and emphasizing the need for inclusive language technology development.

Contribution

It provides empirical data on SES-related differences in language technology use, highlighting the importance of considering socioeconomic factors in AI development.

Findings

Higher SES users use more abstract and concise language.

Lower SES users anthropomorphize LLMs more.

SES differences may exacerbate the digital divide.

Abstract

Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.