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Data Feminism in Personal Data Practice

This is a living document.

It is intended as an initial suggestion and conversation-starter for how the principles of data feminism applies to personal data practice, included in this recipe and drawn from Catherine D'Ignazio and Lauren F. Klein's 2020 book, which you can access for free here:

When we work with our own data, how can this exploration be a playground to explore and apply these principles:

1. Examine Power

Self-tracking tools encode internalized norms, self-discipline.

There is a paradox of control in self-tracking, where the motivations for self-tracking include gaining control, but available methods entail losing control. This paradox emerges from sociotechnical history and neoliberal ethos. (More on this here, particularly Section 3.)

2. Challenge Power

How does your message resist (or embed) cultural and gendered norms?

Sanders (2017 Self-tracking in the digital era: Biopower, patriarchy, and the new biometric body projects. Body & Society, 23(1), 36-63. suggests "potentially subversive body projects by counterposing them to conventional self-improvement projects" (p.21-22) as follows:

  1. instead of an emphasis to use "self-knowledge through numbers" to "discover an authentic self has always already existed," Sanders suggests users "treat digital self-tracking devices not as means of self-discovery but as tools for inventing oneself as something new and not yet imagined"
  2. instead of "body projects" that "define progress, success, and satisfaction in terms of the exterior form of the body" - "counter-normative and more liberating digital body project would perhaps be purposefully goal-unoriented"
  3. instead of "game design elements" which in practice “do not make self-tracking endeavors truly fun, playful, or pleasurable" - "focus on the quality of one’s interior experience during exercise, thereby adopting a counter-normative way of experiencing the body and evaluating how one feels"

3. Elevate Emotion and Embodiment

There is no neutral. What makes visualizations look neutral or comprehensive, even when they are not? What visual tools are available to make the representation show the uncertainty it embeds?

"I am not interested in seamlessness, I’m more interested in seeing what I can get out of a technology that I fed an oral history to.” - Stephanie Dinkins on Not The Only One

4. Rethink Binaries and Hierarchies

“What gets counted counts”

5. Embrace Pluralism

“…the most complete knowledge comes from synthesizing multiple perspectives”

6. Consider Context

Consider multiple audiences – yourself and who else? How can working with personal data become a collective project?

7. Make Labor Visible

Including invisible labor of data work: document process.

Consider Data Work.

  • How much time & effort does it take?
  • How much time & effort should it take?
  • (Whose time? Whose effort?)

"…certain assumptions remain consistent [in popular] articulations about the need for tidiness, cleanliness, and order [in data science.] [Data scientists] must be able to tame the chaos … must “scrub” and “cleanse” dirty data. …But what might be lost in the process of dominating and disciplining data? Whose perspectives might be lost in that process? And, conversely, whose perspectives might be additionally imposed?" ... The assumed values of “cleanliness and control over messiness and complexity” are “not the requirements, nor the goals of all data projects. (Data Feminism, p. 131)

The maintenance data work is "profoundly undervalued in proportion to the knowledge it helps to create" (Data Feminism, p. 180-184). See also: Irani, Lilly. "Justice for ‘Data Janitors’." Public Culture 15 (2015).

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