To Log or Not to Log? SWOT Analysis of Self-Tracking

Li, Na; Hopfgartner, Frank

doi:10.1007/978-3-658-13137-1_17

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Risks such as the spread of misinformation (Albrecht, 2016) or use-errors and resulting wrong treatments (Israelski & Muto, 2012) have also only been theoretically derived so far. In their SWOT (strengths, weaknesses, opportunities, and threats) analysis, Li and Hopfgartner (2016) recognize over-tracking and erosion of privacy as weaknesses and negative societal consequences in terms of privacy as a threat of self-tracking applications.…”

Section: Algorithmic Self-tracking: Risks and Coping Strategies From A User Perspectivementioning

confidence: 99%

Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

Festic

Latzer

Smirnova

2021

MaC

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Self-tracking with wearable devices and mobile applications is a popular practice that relies on automated data collection and algorithm-driven analytics. Initially designed as a tool for personal use, a variety of public and corporate actors such as commercial organizations and insurance companies now make use of self-tracking data. Associated social risks such as privacy violations or measurement inaccuracies have been theoretically derived, although empirical evidence remains sparse. This article conceptualizes self-tracking as algorithmic-selection applications and empirically examines users’ risk awareness related to self-tracking applications as well as coping strategies as an option to deal with these risks. It draws on representative survey data collected in Switzerland. The results reveal that Swiss self-trackers’ awareness of risks related to the applications they use is generally low and only a small number of those who self-track apply coping strategies. We further find only a weak association between risk awareness and the application of coping strategies. This points to a cost-benefit calculation when deciding how to respond to perceived risks, a behavior explained as a privacy calculus in extant literature. The widespread willingness to pass on personal data to insurance companies despite associated risks provides further evidence for this interpretation. The conclusions—made even more pertinent by the potential of wearables’ track-and-trace systems and state-level health provision—raise questions about technical safeguarding, data and health literacies, and governance mechanisms that might be necessary considering the further popularization of self-tracking for health.

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Section: Algorithmic Self-tracking: Risks and Coping Strategies From A User Perspectivementioning

confidence: 99%

Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

Festic

Latzer

Smirnova

2021

MaC

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“…La popularización de esta práctica ha generado posiciones enfrentadas. Algunas voces la defienden como un producto de la curiosidad humana (Wolf, 2010) con gran potencial para el autoconocimiento, para el refuerzo de la memoria personal y colectiva, y para la investigación científica Hopfgartner, 2016). Otras, en cambio, la ven como una tendencia egocéntrica que plantea riesgos importantes como la exposición permanente a la red o la pérdida de control sobre información personal de carácter privado (Gordo-López; De-Rivera; López-Losada, 2013).…”

Section: Algunos Riesgosunclassified

“…Aunque algunos dispositivos de seguimiento almacenan los datos de manera local, solo en sí mismos, la mayoría de los servicios requieren que los usuarios carguen y compartan en servidores remotos los datos que registran sus aplicaciones. Pese a las promesas de seguridad y privacidad, el almacenamiento de datos personales en espacios remotos presenta riesgos y debe considerarse con cautela, especialmente porque pueden ir asociados a datos personales adicionales (edad, situación sentimental u otros detalles confidenciales) que los usuarios tuvieron que proporcionar para aprovechar al máximo el servicio prestado Hopfgartner, 2016).…”

Section: Algunos Riesgosunclassified

Lifelogging: el fenómeno de las “cajas negras” personales

Franganillo¹

2020

ThinKEPI

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Resumen: El lifelogging permite a las personas el registro digital de numerosos aspectos de la propia vida diaria. Traducido como "registro de las actividades vitales", suele practicarse con diversos grados de detalle y para una amplia variedad de propósitos. Es un registro continuo que actúa como apoyo a la memoria o incluso como sustituto de ella, y que constituye así, de alguna manera, una "caja negra", exhaustiva y rigurosa, de ciertas actividades cotidianas. Ofrece, en consecuencia, un gran potencial para extraer conocimiento sobre cómo vivimos. Aunque la "captura total" parece por ahora irrealizable, sí es posible generar registros que, a pesar de ser inevitablemente parciales o fragmentados, resultan de gran utilidad en un buen número de escenarios en los que una persona puede inferir conocimiento sobre sí misma para su propio beneficio. Pero se debe tener presente que el almacenamiento de estos datos personales en espacios remotos presenta ciertos riesgos y debe entonces considerarse con cautela.

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“…In the field of lifelogging, personal sensor data (especially camera or audio data) will carry privacy concerns [10,4]. Therefore, we must consider the principles of privacy-bydesign when creating the test collection.…”

Section: Privacy By Designmentioning

confidence: 99%

NTCIR Lifelog

Gurrin

Joho

Hopfgartner

et al. 2016

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Test collections have a long history of supporting repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists for IR systems that are designed to index and retrieve multimodal lifelog data. In this paper we introduce the first test collection for personal lifelog data, which has been employed for the NTCIR12-Lifelog task. In this paper, the requirements for the test collection are motivated, the process of creating the test collection is described, along with an overview of the test collection. Finally suggestions are given for possible applications of the test collection.

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To Log or Not to Log? SWOT Analysis of Self-Tracking

Cited by 6 publications

References 32 publications

Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

Lifelogging: el fenómeno de las “cajas negras” personales

NTCIR Lifelog

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