Rupa S. Valdez

Denis Newman-Griffis, Bonnielin Swenor, Rupa Valdez et al.

Data are the medium through which individuals' identities and experiences are filtered in contemporary states and systems, and AI is increasingly the layer mediating between people, data, and decisions. The history of data and AI is often one of disability exclusion, oppression, and the reduction of disabled experience; left unchallenged, the current proliferation of AI and data systems thus risks further automating ableism behind the veneer of algorithmic neutrality. However, exclusionary histories do not preclude inclusive futures, and disability-led visions can chart new paths for collective action to achieve futures founded in disability justice. This chapter brings together four academics and disability advocates working at the nexus of disability, data, and AI, to describe achievable imaginaries for artificial intelligence and disability data justice. Reflecting diverse contexts, disciplinary perspectives, and personal experiences, we draw out the shape, actors, and goals of imagined future systems where data and AI support movement towards disability justice.

Sarah Masud Preum, Kate Clark, Ashley Davis et al.

Effective patient education is critical for managing Type 2 Diabetes Mellitus (T2DM), one of the most common chronic diseases in the United States. While some studies focus on the information-seeking behavior of T2DM patients, other self-education behaviors including information processing and utilization are rarely explored in the context of T2DM. This study sought to assess two self-education behaviors of type 2 diabetics, namely, information seeking and information processing, to understand more about how these behaviors affect the self-management of this common chronic disease. Semi-structured interviews were conducted with 8 English speaking T2DM patients and qualitative content analysis techniques were performed to analyze their responses. The information seeking and processing behaviors vary across individuals based on their prognosis of T2DM, information needs, and personal preferences. Patients are often dissatisfied with information from official sources, have difficulty evaluating the trustworthiness of information sources, and desire information that is more personally relevant to them. Several participants identified a lack of personalized information as a key factor in the inability to adhere to T2DM management guidelines, which led them to experience increased glucose levels, difficulty managing A1C levels, frustration, and anxiety. They mentioned that they followed trial and error based approaches to tailor information according to their needs and physiological conditions. Many participants identified conflicting or inconsistent information from different sources as a major barrier to information processing. The results of this study indicate a need for authentic, consistent, and individualized information for type 2 diabetics.