‘There’s nothing really they can do with this information’: unpacking how users manage privacy boundaries for personal fitness information

Zimmer, Michael; Kumar, Priya; Vitak, Jessica; Liao, Yuting; Kritikos, Katie Chamberlain

doi:10.1080/1369118x.2018.1543442

Cited by 50 publications

(61 citation statements)

References 28 publications

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“…As noted in [10], privacy concerns are concrete manifestations of this type of uncertainty, and this appears to be the one aspect of the ethics of WI on which there is already a considerable amount of published research, e.g. [11]- [15].…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Black Box of Wearable Intelligence: Ethical Considerations and Social Impact

Tuovinen

Smeaton

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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Computational intelligence is making its way into a variety of popular consumer products, including wearable physiological monitors such as activity trackers and sleep trackers. Such products are very convenient for the user, but this convenience is the result of a trade-off that has ethical implications, since in almost all cases it denies the user access to their own raw data underlying the easy-to-understand analyses that the products generate for them. One problem with this is that the user is not made aware of the uncertainty of the conclusions or analyses drawn from the data; another is that it is difficult for the user to reuse his or her data in other contexts, such as to combine data from multiple sources. Even if the user did have full control of the data, this would only solve part of the problem, because most people do not have the special skills required to analyze such data. This overall problem could be solved through collaboration between the data owner and a data analysis expert, though this again introduces further problems, notably that of preserving the data owner's privacy. In this paper we analyze the aforementioned issues pertaining to the ethics of wearable intelligence, propose possible approaches to handling them, and discuss the potential social impact of the technology if the issues can be successfully overcome.

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Section: Introductionmentioning

confidence: 99%

Unlocking the Black Box of Wearable Intelligence: Ethical Considerations and Social Impact

Tuovinen

Smeaton

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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“…Some researchers have examined security and privacy more broadly, by looking at a range of existing and emerging technologies [61,69], while others have focused on digital apps [67,75] and social media contexts [24]. Less studied are IoT devices [9], wearables [21,33,50,55,59], and fitness trackers [1,3,42,57,58,76,82].…”

Section: Chapter 2 Backgroundmentioning

confidence: 99%

“…Existing research in the area of security and privacy related to fitness trackers includes: investigating users' concerns, use and sharing of data and protection strategies [3,42,76,82]; the perception of risk with various data an assessment of the sensitivity of their data [82]; the valuation of their personal fitness information [76] and knowledge of company data collection policies [3,76]; their perceptions of the benefits and drawbacks of using fitness trackers [3,42,82]; misconceptions associated with the collection of data [42]; willingness to share sensor data versus derived information (for example, accelerometer data versus step information) [64]; sharing preferences and behaviours of fitness and health information [57]; and examining folk theoriesuser beliefs and understandings -of what exactly is collected by sensor data and how these guide users to manage their privacy choices [58]. Others demonstrated to users how their identity can be revealed when fitness tracker and online social network data is merged [1].…”

Section: Fitness Trackersmentioning

confidence: 99%

“…Zimmer et al [82] conducted interviews with 33 Fitbit and Jawbone users. They used criterion sampling to ensure that they had participants with different levels of privacy concern and internet skills.…”

Section: Lowens and Mottimentioning

confidence: 99%

“…Users are generally ill-informed about digital data collection practices associated with fitness and health related wearables [1,42,50,82] and how data can be misused when collected long-term or combined with other data [50]. They are unaware or unsure of what data is collected, how it is used, who owns the data, with whom it is shared [42,76], what is shared and when it is accessed [42], how it is stored [42,76], for how long [42] and if it is anonymized [1].…”

Section: Data Collection Practicesmentioning

confidence: 99%

See 2 more Smart Citations

User Awareness of Privacy Risks Related to the Collection of Fitness Tracker Data

Gabriele¹

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Personal data collected by fitness trackers can leave users open to security and privacy threats, often without their knowledge. We explored whether increasing user awareness of security and privacy risks might prompt users to take action to protect their personal information. First, we conducted an online survey with 212 fitness tracker users to understand participants' knowledge, attitudes and behaviours related to security and privacy with their fitness trackers. We designed information posters based on our results and conducted a second in-person study with 34 participants. Overall, we found users have distinct sharing preferences for specific types of data and for specific recipients; and they exhibit contradictory behaviour. We demonstrate that it is possible to change fitness tracker users' reported privacy behaviours by showing them information posters. Overall, our findings show fitness tracker users require a greater awareness of protection practices and can benefit, if provided with information. ii iii List of Tables viii List of Figures ix Chapter 1 Please indicate the fitness tracker graph data you would be comfortable sharing with each of the groups of people. (Check all that apply).

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Insights from People’s Experiences with AI: Privacy Management Processes

Walters

Markazi

2021

Diversity, Divergence, Dialogue

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Given the lack of interview-based research in the Artificial Intelligence (AI) literature, we conducted 15 semi-structured interviews about people’s experiences with AI. From those interviews, many important themes emerged and we focused this paper on people’s experiences with privacy violations. We used the Communication Privacy Management Theory (CPM) to analyze distancing behaviors (avoidance or withdrawal) resulting from Boundary Turbulence (privacy violations) with people’s voice-activated phones and Google Search. Through this analysis, we found evidence that distancing behaviors may 1.) be able to transfer from one device to another; and 2.) not depend on the trustworthiness of a particular device or application company. This paper concludes with recommendations for applying the CPM framework more intentionally and rigorously to people’s experiences with voice-activated systems.

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‘There’s nothing really they can do with this information’: unpacking how users manage privacy boundaries for personal fitness information

Cited by 50 publications

References 28 publications

Unlocking the Black Box of Wearable Intelligence: Ethical Considerations and Social Impact

Unlocking the Black Box of Wearable Intelligence: Ethical Considerations and Social Impact

User Awareness of Privacy Risks Related to the Collection of Fitness Tracker Data

Insights from People’s Experiences with AI: Privacy Management Processes

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