Exploring Perceptions of Cross-Sectoral Data Sharing with People with Parkinson’s

McNaney, Róisín; Morgan, Catherine; Kulkarni, Prashant; Vega, Julio; Heidarivincheh, Farnoosh; McConville, Ryan; Whone, Alan; Kim, Mickey; Kirkham, Reuben; Craddock, Ian J

doi:10.1145/3491102.3501984

Cited by 8 publications

(9 citation statements)

References 33 publications

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“…Some wished to learn more about the role of data in AI to assess the positive impacts better. This strengthens prior anecdotal evidence about data sharing “for the greater good” [ 96 ] indicating the willingness of disabled people to share their data for the benefits of the broader communities [ 102 , 107 ].…”

Section: Discussionsupporting

confidence: 80%

“…Often the reason was behind security measures, with a participant articulating “doubt that they would have controls in place” (P9). Their concerns resonate with public perceptions reported in anecdotal evidence — e.g ., small companies were seen to be less stable [ 96 ]. Large technology companies were also perceived as profit driven [ 58 ], which could explain their lower comfort ratings (median=4) towards them compared to organizations supporting education/research or disability communities.…”

Section: Resultsmentioning

confidence: 99%

“…All the concerns related to potential risks of data sharing practices make discussions and frameworks around data ethics more pressing. Recently, there is a body of work within and beyond accessibility to involve potential data contributors in the data collection or sharing contexts to inform future practices [ 50 , 55 , 96 , 102 , 104 , 107 , 119 ]. In addition, these prior efforts surfaced a variety of situational and contextual factors that could influence the contributors’ judgments of concern and risk.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we add new dimensions to these discussions by enabling potential data contributors to reflect on the contexts of sharing data sourced from settings where the technology is being deployed ( i.e ., home). Furthermore, prior efforts investigating concerns of disabled people to inform better technical and legal frameworks for data stewardship ( e.g ., [ 96 , 107 ]) are conducted with regards to simulated datasets and environments. Prior literature has warned about the impact of direct vs. indirect experiences on the development of knowledge, attitudes, and behavior [ 37 ], which can engender differing opinions on issues such as privacy [ 85 ] or risk beliefs [ 142 ].…”

Section: Related Workmentioning

confidence: 99%

“…We focus on the blind community and image data, a challenging scenario where those contributing the data may not be able to inspect them when deciding to share. We designed a pair of studies that aim to surface blind participants’ perspectives on data sharing in a situated, rather than simulated ( e.g ., [ 55 , 96 , 107 ]), context. To achieve this, we teamed up with researchers who were interested in evaluating an AI-infused application in the homes of blind participants.…”

Section: Introductionmentioning

confidence: 99%

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Contributing to Accessibility Datasets: Reflections on Sharing Study Data by Blind People

Kamikubo

Lee

Kacorri

2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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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. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Accessibility; • Social and professional topics → People with disabilities; • Security and privacy → Human and societal aspects of security and privacy.

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Section: Discussionsupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%