Building Accessible Information Systems: A Data-Driven Approach
Computer scientists have made progress on many problems in information access: curating large datasets, developing machine learning techniques, building extensive networks, and designing interfaces to navigate various media. However, many of these solutions do not work well for people with disabilities, who total a billion worldwide (and nearly one in five in the US). For example, visual graphics and small text may exclude people with visual impairments, and text-based resources like search engines and text editors may not fully support people using unwritten sign languages. In this talk, I will present several systems that expand and enrich access to information. These systems employ quantitative methods, using large-scale data collected through existing and novel crowdsourcing platforms to solve data scarcity problems and explore design spaces. They also involve people with disabilities in the solution process, to better understand and address accessibility problems.
Danielle Bragg is a postdoctoral researcher at Microsoft Research. Her research focuses on developing computational systems that expand access to information, in particular for people with disabilities (sign language users and low-vision readers). Her work is interdisciplinary, combining human-computer interaction, applied machine learning, and accessibility. She holds a Ph.D. and M.S. in computer science from the University of Washington, and a B.A. in applied mathematics from Harvard University.