Citation:
D’Ignazio, C., & Klein, L. (2020). Data Feminism. MIT Press.
Chapters 5 – Conclusion: Notes and Key Terms
Reflections and Application
The concluding chapters of Data Feminism provide cases and projects that embrace multiplicity, challenge the perceived neutrality of data, and expose the importance of invisible labor. Many of the themes tie strongly to other DH scholarship explored this semester including Safiya Noble's Algorithms of Oppression and Molly O’Hagan Hardy's essay, "Black Printers on White Cards," in the 2016 edition of Debates in Digital Humanities.
Chapter 5: Unicorns, Janitors, Ninjas, Wizards, and Rock Stars
Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing. Principle: embrace pluralism.
Occlusion typically refers to the “problem” that occurs when some marks (like the eviction dots) obscure other important features (like the whole geography of the city).
Embracing pluralism in data science means valuing many perspectives and voices and doing so at all stages of the process—from collection to cleaning to analysis to communication. It also means attending to the ways in which data science methods can inadvertently work to suppress those voices in the service of clarity, cleanliness, and control.
Data settings—term coined by Yanni Loukissas to describe both the technical and the human processes that affect what information is captured in the data collection process and how the data are then structured
Chapter's key questions: What might be gained if we not only recognized but also valued the fact that data work involves multiple voices and multiple types of expertise? What if producing new social relationships—increasing community solidarity and enhancing social cohesion—was valued (and funded) as much as acquiring data?
Key tenet of feminist thinking - the recognition that a multiplicity of voices, rather than one single loud or technical or magical one, results in a more complete picture of the issue at hand.
Embracing the value of multiple perspectives shouldn’t stop with transparency and reflexivity. It also means actively and deliberately inviting other perspectives into the data analysis and storytelling process—more specifically, those of the people most marginalized in any given context.
Questions about big data versus little data or quantitative data versus qualitative data are far too often framed as false binaries. The key question to keep in mind is how we can scale up data for co-liberation in ways that remain careful, community-based, and complex.
Chapter 6: The Numbers Don’t Speak for Themselves
Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis. Principle: consider context.
Big Dick Data is a formal, academic term that we, the authors, have coined to denote big data projects that are characterized by patriarchal, cis-masculinist, totalizing fantasies of world domination as enacted through data capture and analysis. Big Dick Data projects ignore context, fetishize size, and inflate their technical and scientific capabilities.
Rather than seeing knowledge artifacts, like datasets, as raw input that can be simply fed into a statistical analysis or data visualization, a feminist approach insists on connecting data back to the context in which they were produced.
Open data describes the idea that anyone can freely access, use, modify, and share data for any purpose. Can led to zombie data: datasets that have been published without any purpose or clear use case in mind.
There are imbalances of power in the data setting so we cannot take the numbers in the dataset at face value.
Exploring and analyzing what is missing from a dataset is a powerful way to gain insight into the cooking process—of both the data and of the phenomenon it purports to represent.
Refusing to acknowledge context is a power play to avoid power.
Subjugated knowledge describes the forms of knowledge that have been pushed out of mainstream institutions.
It’s not just in the stages of data acquisition or data analysis that context matters. Context also comes into play in the framing and communication of results.
· emerging practice that attempts to better situate data in context is the development of data user guides.
· "Infomediaries" might include librarians, journalists, nonprofits, educators, and other public information professionals
When numbers derive from a data setting influenced by differentials of power, or by misaligned collection incentives, they run the risk not only of being arrogantly grandiose and empirically wrong, but also of doing real harm in their reinforcement of an unjust status quo.
Chapter 7: Show Your Work
The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued. Principle: make labor visible.
We are less often exposed to the networks of processes and people that help constitute the visualization itself.
· much of the work that goes into designing a data product—visualization, algorithm, model, app—remains invisible and uncredited
Data science is able to profit and thrive because of unpaid invisible labor.
Cultural data workers are responsible for the invisible labor involved in moderating the veritable deluge of content produced online every day.
When designing data products from a feminist perspective, we must aspire to show the work involved in the entire lifecycle of the project
In addition to the invisible labor of data work, there is also labor that remains hidden because we are not trained to think of it as labor at all. This is what is known as emotional labor.
· The notion of emotional labor was supplemented by a related concept, affective labor, so that the work of projecting a feeling (the definition of emotion) could be distinguished from the work of experiencing the feeling itself (the definition of affect).
Data work is part of a larger ecology of knowledge, one that must be both sustainable and socially just.
An emphasis on labor opens the door to the interdisciplinary area of data production studies: taking a data visualization, model, or product and tracing it back to its material conditions and contexts, as well as to the quality and character of the work and the people required to make it.
Showing the work is crucial to ensure that undervalued and invisible labor receives the credit it deserves, as well as to understand the true cost and planetary consequences of data work.
Conclusion
Intersectional feminism—a vibrant body of knowledge and action that challenges the unequal distribution of power—and how it can be applied to the field of data science today.
The authors' definition of data science includes more than quantitative methods, more than “big” data, more than “artificial” intelligence, and more than “neutral” displays.
The best time for resistance and reimagination is before the norms and structures and regulations of the data economy have been fully determined.