Datawear

Skatova, Anya; Shipp, Victoria; Spacagna, Lee; Bedwell, Ben; Beltagui, Ahmad; Rodden, Tom

doi:10.1145/2702613.2725450

Cited by 15 publications

(4 citation statements)

References 8 publications

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“…This points to a need for developing research practices that utilise remote data capture in the service of contextually grounded participant reflections. One example of this is the Datawear app (Skatova et al 2015), which captures data through a camera and elicits reflections on images through experience sampling on a mobile phone. Skatova et al (2015) carefully navigate ethical ideas around private and public by developing a practice of self-reflection where all personal information is stored locally on the phone and only reflections generated by the participant are sent to the researcher.…”

Section: In-situ Research Methodsmentioning

confidence: 99%

“…Situated data gathering in particular tends to become both personally identifying and intimate (Canzian and Musolesi 2015;Montjoye et al 2013). This affected our choice of sensors; where previous Thing Ethnography and experience sampling work used broad data capture devices such as cameras and microphones (Chang et al 2017;Giaccardi 2016a;Giaccardi 2016b;Skatova et al 2015), we used sensors that were minimally identifying. Furthermore, understanding how that data can benefit and create value for industry raises questions about consumer exploitation, as raised by Zuboff (2015).…”

Section: Data Gathering and Participant Experiencesmentioning

confidence: 99%

“…Here participant agency could take on a multitude of different processes that shift power from the researcher and manufacturer to the participants, primarily in understanding the data streams they generate and in being able to control them (Kennedy, Poell and Dijck 2015). One way explored by the HCI community is to store raw data locally and to give users the ability to only share reflections or the results of algorithmic processing (Crabtree et al 2018;Skatova et al 2015). For the purposes of our method, only sharing reflections as initiated by the participant diminishes co-creativity and the ability of the researcher to identify moments of interest and engage in conversation alongside those moments.…”

Section: Data Gathering and Participant Experiencesmentioning

confidence: 99%

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Supporting Real‐Time Contextual Inquiry through Sensor Data

Gorkovenko

Burnett

Murray-Rust

et al. 2019

Ethnographic Praxis

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A key challenge in carrying out product design research is obtaining rich contextual information about use in the wild. We present a method that algorithmically mediates between participants, researchers, and objects in order to enable real-time collaborative sensemaking. It facilitates contextual inquiry, revealing behaviours and motivations that frame product use in the wild. In particular, we are interested in developing a practice of use driven design, where products become research tools that generate design insights grounded in user experiences. The value of this method was explored through the deployment of a collection of Bluetooth speakers that capture and stream live data to remote but co-present researchers about their movement and operation. Researchers monitored a visualisation of the real-time data to build up a picture of how the speakers were being used, responding to moments of activity within the data, initiating text conversations and prompting participants to capture photos and video. Based on the findings of this explorative study, we discuss the value of this method, how it compares to contemporary research practices, and the potential of machine learning to scale it up for use within industrial contexts. As greater agency is given to both objects and algorithms, we explore ways to empower ethnographers and participants to actively collaborate within remote real-time research.

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Section: In-situ Research Methodsmentioning

confidence: 99%

Section: Data Gathering and Participant Experiencesmentioning

confidence: 99%

Section: Data Gathering and Participant Experiencesmentioning

confidence: 99%

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Supporting Real‐Time Contextual Inquiry through Sensor Data

Gorkovenko

Burnett

Murray-Rust

et al. 2019

Ethnographic Praxis

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“…In-situ studies and experience samples are beneficial for gaining insights into the user's perspective, for example, about how the use of wearable technologies may ultimately violate the privacy of non-users [46,51,56,57,78]. However, unique challenges arise when HCI research takes place in natural contexts, where technologies and experiences are evaluated in-situ and where there is less control over the experiences and behaviors of human participants [19,22,23,72].…”

Section: Ethics and Values Research In Hcimentioning

confidence: 99%

Biometric Mirror

Wouters

Kelly

Velloso

et al. 2019

Proceedings of the 2019 on Designing Interactive Systems Conference

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Facial analysis applications are increasingly being applied to inform decision-making processes. However, as global reports of unfairness emerge, governments, academia and industry have recognized the ethical limitations and societal implications of this technology. Alongside initiatives that aim to formulate ethical frameworks, we believe that the public should be invited to participate in the debate. In this paper, we discuss Biometric Mirror, a case study that explored opinions about the ethics of an emerging technology. The interactive application distinguished demographic and psychometric information from people's facial photos and presented speculative scenarios with potential consequences based on their results. We analyzed the interactions with Biometric Mirror and media reports covering the study. Our findings demonstrate the nature of public opinion about the technology's possibilities, reliability, and privacy implications. Our study indicates an opportunity for case study-based digital ethics research, and we provide practical guidelines for designing future studies.

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