@inproceedings{3b7b21ba0670471daa9100d1e60cbc82,
title = "MAIDR Meets AI: Exploring Multimodal LLM-Based Data Visualization Interpretation by and with Blind and Low-Vision Users",
abstract = "This paper investigates how blind and low-vision (BLV) users interact with multimodal large language models (LLMs) to interpret data visualizations. Building upon our previous work on the multimodal access and interactive data representation (MAIDR) framework, our mixed-visual-ability team co-designed maidrAI, an LLM extension providing multiple AI responses to users' visual queries. To explore generative AI-based data representation, we conducted user studies with 8 BLV participants, tasking them with interpreting box plots using our system. We examined how participants personalize LLMs through prompt engineering, their preferences for data visualization descriptions, and strategies for verifying LLM responses. Our fndings highlight three dimensions afecting BLV users' decision-making process: modal preference, LLM customization, and multimodal data representation. This research contributes to designing more accessible data visualization tools for BLV users and advances the understanding of inclusive generative AI applications.",
keywords = "Accessibility, Blind, Data Visualization, Generative AI, Large Language Models, Low Vision, Multimodality, Screen Readers",
author = "Seo, \{Joo Young\} and Kamath, \{Sanchita S.\} and Aziz Zeidieh and Saairam Venkatesh and Sean McCurry",
note = "We express our sincere gratitude for the generous funding provided by the Institute of Museum and Library Services (IMLS) through the Laura Bush 21st Century Librarian Program (grant \#RE-254891-OLS-23), supporting the frst author's early career project. Additionally, we deeply appreciate the invaluable participation and contributions from members of the Blind Academics and National Federation of the Blind mailing listservs.; 26th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2024 ; Conference date: 28-10-2024 Through 30-10-2024",
year = "2024",
month = oct,
day = "27",
doi = "10.1145/3663548.3675660",
language = "English (US)",
series = "ASSETS 2024 - Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility",
publisher = "Association for Computing Machinery",
booktitle = "ASSETS 2024 - Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility",
address = "United States",
}