AccessShare: Co-designing Data Access and Sharing with Blind People

TL;DR

This paper presents AccessShare, an accessible data access interface co-designed with blind users to improve data inspection, consent, and control in AI datasets, fostering inclusivity and responsible data practices.

Contribution

It introduces a novel accessible data inspection system, AccessShare, co-designed with blind users, addressing a critical gap in inclusive AI data collection and management.

Findings

Interactive informed consent enhances user understanding.

AccessShare facilitates communication between data stewards and blind contributors.

Design insights inform future inclusive data practices.

Abstract

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.