A11y-CUA Dataset: Characterizing the Accessibility Gap in Computer Use Agents
Ananya Gubbi Mohanbabu, Rosiana Natalie, Brandon Kim, Anhong Guo, Amy Pavel
Abstract
Computer Use Agents (CUAs) operate interfaces by pointing, clicking, and typing—mirroring interactions of sighted users (SUs) who can thus monitor CUAs and share control. CUAs do not reflect interactions by blind and low-vision users (BLVUs) who use assistive technology (AT). BLVUs thus cannot easily collaborate with CUAs. To characterize the accessibility gap of CUAs, we present A11y-CUA, a dataset of BLVUs and SUs performing 60 everyday tasks with 40.4 hours and 158,325 events. Our dataset analysis reveals that our collected interaction traces quantitatively confirm distinct interaction styles between SU and BLVU groups (mouse- vs. keyboard-dominant) and demonstrate interaction diversity within each group (sequential vs. shortcut navigation for BLVUs). We then compare collected traces to state-of-the-art CUAs under default and AT conditions (keyboard-only, magnifier). The default CUA executed 78.3% of tasks successfully. But with the AT conditions, CUA’s performance dropped to 41.67% and 28.3% with keyboard-only and magnifier conditions respectively, and did not reflect nuances of real AT use. With our open A11y-CUA dataset, we aim to promote collaborative and accessible CUAs for everyone.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Builds on20
- WebShop: Towards Scalable Real-World Web Interaction with Grounded Language AgentsShunyu Yao, Howard Chen, John Yang, Karthik NarasimhanNeurIPS 2022 · 1,477 citations
- WebArena: A Realistic Web Environment for Building Autonomous AgentsShuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou et al.ICLR 2024 · 1,197 citations
- GAIA: a benchmark for General AI AssistantsGrégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun et al.ICLR 2024 · 716 citations
- GPT-4V(ision) is a Generalist Web Agent, if GroundedBoyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun et al.ICML 2024 · 496 citations
- Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case StudyPerttu Hämäläinen, Mikke Tavast, Anton KunnariCHI 2023 · 244 citations
Related papers
- Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind UsersFarnaz Zamiri Zeraati, Yang Trista Cao, Yuehan Qiao, Hal Daumé III et al.CHI 2026 · 1 citation
- Toward Independent Online Shopping of the Visually Impaired Through Voice-based Computer-Using AgentSubin Shin, Jeesun Oh, Suhyun Kim, Seoyeon Eom et al.CHI 2026 · 1 citation
- ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform DataZhaoyang Liu, Jingjing Xie, Zichen Ding, Zehao Li et al.ICLR 2026 · 54 citations
- VideoA11y: Method and Dataset for Accessible Video DescriptionChaoyu Li, Sid Padmanabhuni, Maryam S. Cheema, Hasti Seifi et al.CHI 2025 · 23 citations
- Examining Visual Semantic Understanding in Blind and Low-Vision Technology UsersVenkatesh Potluri, Tadashi E. Grindeland, Jon E. Froehlich, Jennifer MankoffCHI 2021 · 42 citations
