Add Thesis

Ethical Reflections on Quantified Self Devices and their Effects on Humans

Written by N. Berg

Paper category

Master Thesis


Computer Science




Master Thesis: The self-exploration dge is usually implicit, self-evident and invisible [29], so it is difficult to obtain without technical assistance. Therefore, quantitative self-equipment helps to reflect certain information and its patterns to users, thereby opening up opportunities for self-exploration and self-optimization. Sociology reveals three risks in the use of quantified self-technology [29]. Once, the effect of permanent monitoring. The research by the monitors has a long history. Second, the loss of self-control is conducive to the external control of equipment and companies, for example, when the machine determines who gets credit at the bank and who does not. Third, the loss of recognition of human beings as "human beings" supports the view that human beings are cumulative numbers. For example, when people are viewed in a statistical way, for those who have not or are misrepresented by the digital system, they are not individuals with different needs and goals. The details of the world are invisible and lack fairness. Another problem is the change in human interaction [29]. For example, the performance society increases the individual's anxiety about their performance and affects people's beliefs about the ability to persevere in this society. Therefore, people are increasingly comparing themselves with related groups. Quantitative self technology is used collectively in sports, self-help groups, and between friends and family. The implicit assumption is that the extra external motivation of social comparison helps to achieve an optimized version of self. This chapter outlines the quantitative self, its definition, its users, and its technology. These aspects and their impact will be discussed in more depth in later chapters. 3.1 Quantifying self The impulse to measure yourself has a long history. Diet tracking can be traced back to ancient times [29]. The first technical device to support self-tracking was a 16th century mechanical scale used to measure the weight of a user. At that time, Santorius of Padua tracked body weight and food intake for 30 years to study metabolism. Today, more complex devices support the quantified self. The fitness tracker measures steps, heartbeat, sleep and temperature. There are smartphone apps to track diet, weight, mood, habits or productivity (or at least the time dedicated to work). The goal of quantitative self-practitioners is to explore themselves in self-reflection. The device-oriented quantitative self movement began in the 20th century, when the size of computers became smaller and smaller. At first, a strong motivation of early practitioners was to gain independence from their own body, independent of medical expert knowledge [29]. For this work, we define quantitative self as the activity of representing humans in digital form. The representation need not be comprehensive. There is also a subset of values ​​that can reveal enough information for the point. A quantified self device (or application) is a technical entity, ie. 5 Impact on users In order to analyze the predictable impact of smart clothing on users, we look at concepts in psychology and sociology, which we summarize in this chapter. We use these concepts to understand how smart clothing builds its influence and how users’ feelings, behaviors, and relationships are affected. We look at some concepts that allow us to understand the effectiveness of the quantified self, in order to realize its self-declared advantages and the possible side effects of the use of the quantified self. 5.1 Motivation Motivation plays an important role as the basis of behavior [17]. The reasons why people track their physical activity, emotional state, sleep or food can be very different. Their motives can be internal or external, and belong to the standards and goals people set for themselves. Their motivation can be short-term or long-term, and can change over time. Therefore, technologies (such as big data applications) look for stimuli that motivate people to take certain actions (such as buying something). When these stimuli are used more and more frequently and obtain sufficient success, users will begin to expect them. Other types of motivation, such as altruism or empathy, will not get the same benefits from optimization and risk being marginalized. In the case of quantified self-equipment, motivation has two main goals: improving behavior awareness and optimizing behavior [34, 29]. Which body function or behavior is tracked depends on the underlying motivation. Lupton [45] identified five different motivations and described how self-tracking finds its own way in users’ lives: Private self-tracking is based on personal goals to increase self-awareness or optimize their lives (self-improvement hypothesis [25]). The collected data is only used privately or shared with selected other people, such as friends. Similarly, Mämecke [46] saw the motivation of early self-trackers to reduce dependence on the “paternalistic” healthcare system in liberating efforts. It is up to the user to choose and adopt it into the user's life. The goal of Pushself-tracking is to stimulate self-determined behavior changes. To this end, the collected data is visualized in a motivating manner to stimulate emotional responses that ultimately lead to behavioral changes. Public self-tracking involves sharing the collected data as part of a social movement. Therefore, a motive is to belong to a social group. Communities can be created in different contexts, for example, local or interest-based, city-wide, or all joggers in the same age range. In the dressing scene, public self-tracking occurs in comparisons between the health levels of different companies and their employees. These systems are implemented as a top-down structure, in which the company determines how the system works. Read Less