LLMs are increasingly integrated into writing workflows worldwide. This project explores how autocomplete writing suggestions affect users from diverse cultural backgrounds. In a controlled study with 118 participants from India and the United States, we investigated whether these tools disproportionally benefit Western users and homogenize non-Western users’ writing styles toward Western norms. We found that while AI-based suggestions can boost overall writing productivity, the gains are not distributed equally: non-Western users have to invest more effort to adapt culturally incongruent suggestions, leading to less net benefit. Moreover, these suggestions subtly steer non-Western users toward Western writing styles, risking cultural erasure and reducing linguistic diversity. This study underscores the need for culturally responsive LLMs that accommodate diverse cultural and linguistic practices.

Multilingual NLP aims to serve diverse user communities worldwide, spurring the development of both massively multilingual models that handle hundreds of languages and localized models that cater to a specific linguistic community. However, fluency in a local language does not inherently guarantee cultural understanding. As a result, even specialized localized models may fail to capture the full range of cultural knowledge, values, and practices they claim to represent. It therefore remains unclear whether multilingual LLMs are truly “multicultural.” To shed light on this question, we compared the cultural appropriateness of Indic models—designed to better represent Indian user needs—to that of more generalized “monolithic” models. Our findings show that the Indic models do not necessarily reflect the cultural values of Indian users any more accurately than their monolithic counterparts, suggesting that current multilingual training paradigms and datasets do not necessarily produce genuine cultural competence.

Gender bias in large language models (LLMs) – defined as the tendency of these models to reflect and perpetuate stereotypes, inequalities, or prejudices based on gender – has received significant scholarly attention in the last few years. However, only a handful of studies have analysed this issue against the backdrop of India’s sociocultural setting, and almost none (to the best of our knowledge) have looked at it in relation to critical social sectors.
With support from the Gates Foundation, we conducted a one-year exploratory study to investigate gender bias in LLMs customised for Indian languages and deployed in resource-constrained settings. Through key informant interviews with developers, field visits, prompting exercises, and expert workshops, we analysed how gender-related concerns emerge at different stages of the LLM lifecycle.
Our approach moved beyond a narrow, technical perspective that treated gender bias as merely a problem of semantics. Instead, we recognised it as a reflection of deeper structural inequities that required interdisciplinary, context-aware solutions. As part of this effort, we also developed a set of design principles and strategies to mitigate bias, specifically tailored to the realities of the Indian context.

While LLMs are becoming increasingly prevalent in education, their role and impact in non-English learning environments remain largely unexplored. This project investigates the effectiveness and impact of an LLM-based tool designed to support teachers in India. An extensive pilot study was conducted with 1000 teachers in Karnataka, India. The study examines how LLMs assist teachers for whom English is a secondary language, particularly in lesson planning and learning content generation. It also identifies the challenges they encounter, evaluates the tool’s effectiveness, and explores how AI is transforming their workflows. The learnings from the pilot study would inform the design and implementation of the tool for several thousands of teachers in India. By providing empirical evidence and actionable insights, this research aims to inform the design of effective, culturally responsive LLM-based learning technologies for teachers in the Global South.

Most social media users come from non-English speaking countries in the Global South, where a large percentage of harmful content is not in English. However, current moderation systems struggle with low-resource languages spoken in these regions. In this work, we examine the challenges AI researchers and practitioners face when building moderation tools for low-resource languages. We conducted semi-structured interviews with 22 AI researchers and practitioners specializing in automatic detection of harmful content in four diverse low-resource languages from the Global South. These are: Tamil from South Asia, Swahili from East Africa, Maghrebi Arabic from North Africa, and Quechua from South America. Our findings reveal that social media companies’ restrictions on researchers’ access to data exacerbate the historical marginalization of these languages, which have long lacked datasets for studying online harms. Moreover, the status quo prioritizes data-intensive methods for detecting harmful content, overlooking alternative approaches that center linguistic diversity, morphological complexity, and dynamic evolution through code-mixing and code-switching—phenomena largely absent in English. We provide concrete evidence how these underexplored issues lead to critical errors when moderating content in Tamil, Swahili, Arabic, and Quechua, which are morphologically richer than English. Based on our findings, we establish that the precarities in current moderation pipelines are rooted in deep systemic inequities and continue to reinforce historical power imbalances. We conclude by discussing multi-stakeholder approaches to improve moderation for low-resource languages.