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From Design to Data Handling. Why mHealth Needs a Feminist Perspective

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Feminist Philosophy of Technology

Part of the book series: Techno:Phil – Aktuelle Herausforderungen der Technikphilosophie ((TPAHT,volume 2))

Abstract

The emergence of mobile health (mHealth) technologies has been celebrated by many scholars, who argue that mHealth can deliver health services to more people and democratize healthcare. At the same time, innovation with mHealth is being conducted in a world with a significant digital divide and structural inequities. We interrogate mHealth from a gender perspective and argue that while these technologies are novel, they involve some of the major gender issues previously identified in medicine and healthcare more broadly. We suggest that these problems can be addressed by the implementation of an intersectional feminist perspective into all stages of mHealth technology development and provision.

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Notes

  1. 1.

    In this book chapter, we understand healthcare broadly—it not only includes hospitals and private surgeries but also a range of practices or technologies used in the caring for health, including one’s own health.

  2. 2.

    According to the WHO, mHealth is a component of eHealth (WHO Global Observatory for eHealth 2011).

  3. 3.

    This is confirmed with data from the World Economic Forum’s (2018) Global Gender Gap Report, which clarifies that 78% of Artificial Intelligence (AI) professionals globally are male (The Global Gender Gap Report 2018).

  4. 4.

    The issue of bias in AI is quite complex, owing to different understandings of ‘bias’ in statistics and social science approaches (Selbst & Barocas 2017). Selbst and Barocas point out that, for example, the concept of ‘selection bias’ is given traction regarding concerns about errors in estimation, which are produced when a population subgroup is more likely to be sampled. As an example, they cite problems with low accuracy of facial recognition software, trained on data over-representing a particular racial group. They observe that in legal and colloquial language, concerns about ‘bias’ involve worries about judgements grounded in preconceived notions or prejudice, which is closely linked to normative and ethical concerns about fairness and equality. However, according to them, there are no clear boundaries between statistical and normative definitions in practice, as biased models or algorithms can result in unjust treatments and outcomes in different social groups.

  5. 5.

    The term ‘gender diverse people’ refers to identities beyond the gender binary categories of men and women, including non-binary, genderfluid or genderqueer people. The phrasing ‘sex and/or gender diverse people’ acknowledges that some individuals can be both sex and gender diverse (for example intersex and genderfluid), while others might solely be sex or gender diverse.

  6. 6.

    In this regard, Price II (2017: 423) argues for greater regulation of algorithmic medicine. According to him: “Patients and providers must trust that algorithms are safe and effective to rely on them, but they lack the experience or knowledge to evaluate algorithms at the point of care, creating a need for systemic regulation. Regulation can help but must walk a fine line: demonstrating safety and efficacy without destroying the flexibility and ongoing innovation that drive algorithmic medicine’s development.”

  7. 7.

    There are also concerns about the ownership of the data and related power dynamics in mHealth. Building on the concept of “datafication of everything“ (Mayer-Schönberger & Cukier 2013: 94), Minna Ruckenstein and Natasha Dow Schüll (2017) as well as Lupton (2016) raise issues with the ‘datafication’ of health. They argue that digitally collected and stored data are becoming increasingly important. Once body-related data are available in a digital form, data generated in personal and private practices of self-tracking are out of reach of those who generated them and thus belong to commercial entities or governmental organizations (Lupton 2016). This creates an asymmetric relationship between those who produce data and those who process and use them commercially (Ruckenstein & Dow Schüll 2017; boyd & Crawford 2012: 666 f.). In their role as ‘data sources’, individuals perform unpaid and invisible digital work while losing control over the data they create (Ruckenstein & Dow Schüll 2016).

  8. 8.

    LGTBIQ stands for lesbian, gay, transgender, bisexual, intersex and queer.

  9. 9.

    There are some aspects of Daniels’ theory of justice in health, which one author of this chapter (Tereza Hendl) has critically discussed in detail (Hendl 2015). According to Daniels, the core function of healthcare is to protect ‘normal human functioning’, which he perceives through the lens of “species-typical normal functional organisation” (2001: 3, footnote 1). In his view, disease or disability ‘impair’ normal functioning and as such, restrict the pool of opportunities available to individuals. Thus, he sees the moral importance of healthcare in protecting individuals’ fair share of opportunities by keeping them close to normal functioning. While Daniels’ overall argument that healthcare is special because of its implications for opportunity is convincing, his concept of ‘normal human functioning’, particularly the conceptualisation of an ideal human state as an absence of disability can be critically perceived. Building on the work of critical disability scholars (Garland-Thomson 2011; Shakespeare 1998), issues can be raised with a stigmatising outlook on disability and reflect on the negative impact of disableist views and social environment on the lives of people with impairment.

  10. 10.

    Hiring diverse teams also pays off. In the 2015 report “Why diversity matters” Hunt and her colleagues (2015: 1) find, that “[…] companies in the top quartile for gender or racial and ethnic diversity are more likely to have financial returns above their national industry medians.”

  11. 11.

    Many scholars have challenged the very concept of race (Hall et al. 1997; Obasogie 2010; Morning 2014). They have argued that race is not a biologically or genetically significant category but a social construct. While race is not ‘real’ in a biological sense, it is nevertheless real as a category of power and social stratification.

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Authors and Affiliations

  1. Munich, Germany

    Tereza Hendl, Bianca Jansky & Verina Wild

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  1. Tereza Hendl
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  2. Bianca Jansky
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  3. Verina Wild
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Correspondence to Tereza Hendl .

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  1. Institut für Philosophie, Universität Wien, Wien, Austria

    Janina Loh

  2. Institut für Philosophie, Universität Wien, Wien, Austria

    Mark Coeckelbergh

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Hendl, T., Jansky, B., Wild, V. (2019). From Design to Data Handling. Why mHealth Needs a Feminist Perspective. In: Loh, J., Coeckelbergh, M. (eds) Feminist Philosophy of Technology. Techno:Phil – Aktuelle Herausforderungen der Technikphilosophie, vol 2. J.B. Metzler, Stuttgart. https://doi.org/10.1007/978-3-476-04967-4_5

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  • DOI: https://doi.org/10.1007/978-3-476-04967-4_5

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