Kamikubo Rie, Lee Kyungjun, Kacorri Hernisa
College of Information Studies, University of Maryland, College Park, United States.
Department of Computer Science, University of Maryland, College Park, United States.
Proc SIGCHI Conf Hum Factor Comput Syst. 2023 Apr;2023:827. doi: 10.1145/3544548.3581337. Epub 2023 Apr 19.
To ensure that AI-infused systems work for disabled people, we need to bring accessibility datasets sourced from this community in the development lifecycle. However, there are many ethical and privacy concerns limiting greater data inclusion, making such datasets not readily available. We present a pair of studies where 13 blind participants engage in data capturing activities and reflect with and without probing on various factors that influence their decision to share their data via an AI dataset. We see how different factors influence blind participants' willingness to share study data as they assess risk-benefit tradeoffs. The majority support sharing of their data to improve technology but also express concerns over commercial use, associated metadata, and the lack of transparency about the impact of their data. These insights have implications for the development of responsible practices for stewarding accessibility datasets, and can contribute to broader discussions in this area.
为确保融入人工智能的系统适用于残疾人,我们需要在开发生命周期中引入源自该群体的无障碍数据集。然而,诸多伦理和隐私问题限制了更多数据的纳入,使得此类数据集难以获取。我们开展了两项研究,13名盲人参与者参与数据采集活动,并在有无引导的情况下,思考影响他们通过人工智能数据集分享数据的各种因素。我们观察在评估风险与收益的权衡时,不同因素如何影响盲人参与者分享研究数据意愿。大多数人支持分享他们的数据以改进技术,但也对商业用途、相关元数据以及数据影响缺乏透明度表示担忧。这些见解对管理无障碍数据集的负责任做法的发展具有启示意义,并有助于该领域更广泛的讨论。