Accessibility-Driven Information Transformations in Mixed-Visual Ability Work Teams
Yichun Zhao, Miguel A. Nacenta, Mahadeo A. Sukhai, Sowmya Somanath
TL;DR
This paper investigates how mixed-visual ability work teams manage accessibility by examining representational transformations. Through a week-long diary study with 23 BLV and sighted professionals across five teams, the authors identify 36 transformation cases and distill four recurring coordination patterns: Disposable fixes, Transformed becomes the standard, Parallel representations, and Assembly. They show that transformations can both enhance and simplify representations, and that coordination labor is a critical, often inequitable component of accessibility work, influenced by reactive versus proactive triggers. The study offers design opportunities for anticipatory, multi-modal, and collaboratively guided representations to reduce invisible labor and improve inclusive collaboration in real-world settings.
Abstract
Blind and low-vision (BLV) employees in mixed-visual ability teams often encounter information (e.g., PDFs, diagrams) in inaccessible formats. To enable teamwork, teams must transform these representations by modifying or re-creating them into accessible forms. However, these transformations are frequently overlooked, lack infrastructural support, and cause additional labour. To design systems that move beyond one-off accommodations to effective mixed-ability collaboration, we need a deeper understanding of the representations, their transformations and how they occur. We conducted a week-long diary study with follow-up interviews with 23 BLV and sighted professionals from five legal, non-profit, and consulting teams, documenting 36 transformation cases. Our analysis characterizes how teams perform representational transformations for accessibility: how they are triggered proactively or reactively, how they simplify or enhance, and four common patterns in which workers coordinate with each other to address representational incompatibility. Our findings uncover opportunities for designing systems that can better support mixed-visual ability work.