An Autoethnographic Case Study Of Generative Artificial Intelligence's Utility For Accessibility
Kate S Glazko, Momona Yamagami, Aashaka Desai, Kelly Avery MacK, Venkatesh Potluri, Xuhai Xu, Jennifer Mankoff . The 25th International ACM SIGACCESS Conference on Computers and Accessibility 2023 – 57 citations
[Paper]
With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular. To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.