Michał Wieczorek

In this article, we address the case of self-tracking as a practice in which two meaningful backgrounds (physical world and technological infrastructure) play an important role as the spatial dimension of human practices. Using a (post)phenomenological approach, we show how quantification multiplies backgrounds, while at the same time generating data about the user. As a result, we can no longer speak of a unified background of human activity, but of multiple dimensions of this background, which, additionally, is perceived as having no pivotal role in the process, often being hidden, situated beyond human consciousness, or taken for granted. Consequently, the phenomenological experience of the background turns into a hermeneutic practice focused on the interpretation of representations and descriptions. By adopting a (post)phenomenological approach, we show the problems and limitations of quantification of human activities occurring in self-tracking and the theoretical problems associated with the scheme of human-technology relations.

In this paper, I discuss the formation of habits with the help of self-tracking technologies. Although devices like Fitbit smart bands come with promises of empowerment through the means of increased control over users’ habits, existing literature does not provide conclusive findings about the validity of such claims. I contribute to the ongoing debate by relying on John Dewey’s pragmatist philosophy and its notion of intelligent habit. I demonstrate that from a pragmatist standpoint, habits that are the most likely to accomplish users’ goals contribute to their flourishing need to be reflective (accompanied by adequate deliberation) and flexible (adaptable to the changing circumstances). On this basis I highlight some ethical-epistemic issues surrounding self-tracking technologies that inhibit the formation of habits desirable from a Deweyan standpoint. These include a lack of reflection on the part of the developers, difficulties for users to deliberate and consciously shape the habits developed by their devices, and dependence upon self-tracking tools that makes it difficult to adapt habits to individual needs and circumstances. I conclude the paper by discussing self-tracking’s general impact on flourishing, as well as placing my arguments in the context of the diversity of self-tracking practices and identifying how users attempt to alleviate the shortcomings of the technology and make it more suitable to their goals and needs.

This paper discusses how John Dewey’s notion of intelligent habit could contribute to technology ethics. For Dewey, intelligent (i.e., desirable) habits are reflective – arising from inquiry into the appropriate courses of action in each situation – and flexible – easily adaptable to the changing circumstances. We should strive to develop intelligent habits as they are the best tools for the achievement of our goals and are necessary for individual and societal flourishing. I argue that Dewey’s notion of intelligent habit makes it possible to identify the impact of technology on users’ identity and self-determination, advocate for the extension of user choice and malleability of technologies, and analyse the connections between the epistemic dimension of technology and users’ ability to flourish.

This dissertation is devoted to the ethical issues arising in connection to self-tracking practices and technologies. Today, various digital devices (such as Fitbit or Apple Watch) and smartphone apps (e.g.,MyFitnessPal) allow users to collect, quantify and analyse information about their behaviour and bodily parameters, typically for the purposes of behaviour change and management. Self-tracking is commonly associated with the promise of translating quantified data into greater knowledge about oneself, as well as greater control over one’s habits and body. Over the course of the dissertation, I identify the normative issues surrounding selftracking by means of a systematic literature review and adapt John Dewey’s ethical theory to apply it to the analysis of the issues that have not yet been adequately analysed in the literature, namely, the impact on the individual and habit formation, the impact on interpersonal relations, and just distribution of benefits and burdens produced through self-tracking. With the help of Dewey’s ethical ideals of inclusive, continuing growing, and democracy (understood as a cooperative and participatory way of life, rather than a political system), I argue that self-tracking 1) has a negative impact on users’ identity and autonomy by manipulating them into behaviours that are not necessarily in line with their sense of self; 2) limits the quality and depth of users’ engagement with others; and 3) allows companies to exert control over users and profit from their data without adequate compensation, and benefits users from privileged groups to a much greater degree than those coming from marginalised backgrounds. The analysis is accompanied by a set of recommendations for users, developers and policymakers that would ensure that self-tracking positively contributes to individual and societal flourishing/wellbeing and operates in line with the ideal of social justice.

Mobile health (mHealth) technologies for self-monitoring health-relevant parameters such as heart frequency, sleeping patterns or exercise regimes aim at fostering healthy behavior change and increasing the individual users to promote and maintain their health. We argue that this aspect of mHealth supports healthism, the increasing shift from institutional responsibility for public health toward individual engagement in maintaining health as well as mitigating health risks. Moreover, this healthist paradigm leads to a shift from understanding health as the absence of illness to regarding health as the performance of certain rituals in order to project healthiness. By drawing from the analogy between healthiness and traditional virtues, we evaluate the promises made by proponents of mHealth technologies for self-monitoring. We argue that the implementation and use of mHealth risk entrenching existing inequalities and, more particularly, tend to exclude populations situated at the losing end of those inequalities from participating in the quasi-virtue of healthiness. Consequently, the implementation and use of mHealth technologies not only present challenges for social justice but also undermine their primary societal goal—to promote public health. Finally, we offer several suggestions on how to realize the potential benefit of mHealth.

This paper analyzes the ethics of social science research employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: the interpretative character of both SSR and big data, complexities of anticipating and managing risks in publication and reuse of big data SSR, and the paucity of regulatory oversight and ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR.

In this article, we address the case of self-tracking as a practice in which two meaningful backgrounds play an important role as the spatial dimension of human practices. Using a phenomenological approach, we show how quantification multiplies backgrounds, while at the same time generating data about the user. As a result, we can no longer speak of a unified background of human activity, but of multiple dimensions of this background, which, additionally, is perceived as having no pivotal role in the process, often being hidden, situated beyond human consciousness, or taken for granted. Consequently, the phenomenological experience of the background turns into a hermeneutic practice focused on the interpretation of representations and descriptions. By adopting a phenomenological approach, we show the problems and limitations of quantification of human activities occurring in self-tracking and the theoretical problems associated with the scheme of human-technology relations.

This paper presents a literature review on the ethics of self-tracking technologies which are utilized by users to monitor parameters related to their activity and bodily parameters. By examining a total of 65 works extracted through a systematic database search and backwards snowballing, the authors of this review discuss three categories of opportunities and ten categories of concerns currently associated with self-tracking. The former include empowerment and well-being, contribution to health goals, and solidarity. The latter are social harms, privacy and surveillance, ownership control and commodification of data, autonomy, data-facilitated harm, datafication and interpretability of data, negative impact on relation to self and others, shortcomings of design, negative impact on health perception, and regulation and enforcement of rules. The review concludes with a critical analysis of the existing literature and an overview of a future research agenda that could complement the current work on ethics of self-tracking.

AI systems have often been found to contain gender biases. As a result of these gender biases, AI routinely fails to adequately recognize the needs, rights, and accomplishments of women. In this article, we use Axel Honneth’s theory of recognition to argue that AI’s gender biases are not only an ethical problem because they can lead to discrimination, but also because they resemble forms of misrecognition that can hurt women’s self-development and self-worth. Furthermore, we argue that Honneth’s theory of recognition offers a fruitful framework for improving our understanding of the psychological and normative implications of gender bias in modern technologies. Moreover, our Honnethian analysis of gender bias in AI shows that the goal of responsible AI requires us to address these issues not only through technical interventions, but also through a change in how we grant and deny recognition to each other.