AccessShare: Co-designing Data Access and Sharing with Blind People
This work addresses the issue of inaccessible data inspection and control for blind people in AI data collection, which is incremental as it builds on existing co-design methods to propose specific improvements.
The study tackled the problem of inaccessible visual inspection of contributed image data for blind people by co-designing a novel data access interface called AccessShare with 10 blind participants, revealing that interactive informed consent and data inspection systems can facilitate communication between data stewards and blind contributors.
Blind people are often called to contribute image data to datasets for AI innovation with the hope for future accessibility and inclusion. Yet, the visual inspection of the contributed images is inaccessible. To this day, we lack mechanisms for data inspection and control that are accessible to the blind community. To address this gap, we engage 10 blind participants in a scenario where they wear smartglasses and collect image data using an AI-infused application in their homes. We also engineer a design probe, a novel data access interface called AccessShare, and conduct a co-design study to discuss participants' needs, preferences, and ideas on consent, data inspection, and control. Our findings reveal the impact of interactive informed consent and the complementary role of data inspection systems such as AccessShare in facilitating communication between data stewards and blind data contributors. We discuss how key insights can guide future informed consent and data control to promote inclusive and responsible data practices in AI.