Gauri Kambhatla, Sanjana Gautam, Angela Zhang, Alexander Liu, Ravi Srinivasan, Junyi Jessy Li, Matthew Lease. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Our research investigates the impact of Generative Artificial Intelligence (GAI) models, specifically text-to-image generators (T2Is), on the representation of non-Western cultures, with a focus on Indian contexts. Despite the transformative potential of T2Is in content creation, concerns have arisen regarding biases that may lead to misrepresentations and marginalizations. Through a Non-Western community-centered approach and grounded theory analysis of 5 focus groups from diverse Indian subcultures, we explore how T2I outputs to English input prompts depict Indian culture and its subcultures, uncovering novel representational harms such as exoticism and cultural misappropriation. These findings highlight the urgent need for inclusive and culturally sensitive T2I systems. We propose design guidelines informed by a sociotechnical perspective, contributing to the development of more equitable and representative GAI technologies globally. Our work underscores the necessity of adopting a community-centered approach to comprehend the sociotechnical dynamics of these models, complementing existing work in this space while identifying and addressing the potential negative repercussions and harms that may arise as these models are deployed on a global scale.

We investigate the potential for nationality biases in natural language processing (NLP) models using human evaluation methods. Biased NLP models can perpetuate stereotypes and lead to algorithmic discrimination, posing a significant challenge to the fairness and justice of AI systems. Our study employs a two-step mixed-methods approach that includes both quantitative and qualitative analysis to identify and understand the impact of nationality bias in a text generation model. Through our human-centered quantitative analysis, we measure the extent of nationality bias in articles generated by AI sources. We then conduct open-ended interviews with participants, performing qualitative coding and thematic analysis to understand the implications of these biases on human readers. Our findings reveal that biased NLP models tend to replicate and amplify existing societal biases, which can translate to harm if used in a sociotechnical setting. The qualitative analysis from our interviews offers insights into the experience readers have when encountering such articles, highlighting the potential to shift a reader’s perception of a country. These findings emphasize the critical role of public perception in shaping AI’s impact on society and the need to correct biases in AI systems.

Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting-Hao Huang, Shomir Wilson. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Pranav Venkit, Mukund Srinath, Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca Passonneau, Shomir Wilson. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

We examine the major challenges AI experts encounter when deploying AI solutions. We conduct in-depth interviews with AI experts to elicit the frequent challenges they face. Our preliminary results highlight several factors that contribute to the challenging nature of the field, namely data scarcity, uncertainty, ill-structured problems, user behavior, and the application domain. This work indicates important areas that can be targeted during the curriculum design and development process.

Options for students to learn and connect with each other have diversified in recent years, with online resources and campuses playing an increasing role. Flexibility and comfort are becoming a priority as students choose when, where and how to pursue learning goals. Nonetheless, students want to feel sense of community with their peers and instructors; institutional bonds are in turn associated with enhanced learning. In this paper, we explore the feelings of community among students studying at a geographically distributed university. We seek to understand how the students understand community, the levels of community they feel and how their campus location may affect these feelings. In this paper, we present findings from both a survey and an interview study and consider the implications for tools that might promote community.