Week 3 Readings: Data Feminism

Data Feminism by Catherine D’Ignazio and Lauren F. Klein is a readable and thorough entry into how data science needs feminism and how feminists and scholars can use data science to further their goals. Each chapter focuses on one of D’Ignazio and Klein’s seven principles of data feminism.

1. Examine Power

Data science is deeply influenced by unequal power structures, or matrices of domination. Readers are encouraged to ask, “Who?” when thinking about data collection and analysis: Who is doing data science? Who benefits from data science? And whose interests and goals are being served by data science?

By asking “who” questions, we can spot gaps in data collection and analysis and begin to fill those gaps.

2. Challenge Power

D’Ignazio and Klein offer four methods of challenging unjust data science:

  1. Collect: Compile counterdata.
  2. Analyze: Audit algorithms.
  3. Imagine: Imagine a future of co-liberation.
  4. Teach: Engage and empower people to use data science as a tool.

As part of their “imagine” method, the authors also advocate for a shift from data ethics, which tends to frame problems as the result of a few “bad apples” and technological glitches, to data justice, which acknowledges that injustice is structural.

Table 2.1 From data ethics to data justice

Concepts That Secure Power
Because they locate the source of the problem in individuals or technical systems
Understanding Algorithms

Concepts that Challenge Power
Because they acknowledge structural power differentials and work toward dismantling them
Understanding history, culture, and context
Table 2.1 presents principles of data ethics alongside alternative, parallel concepts of data justice (60).
Why is the shift from data ethics to data justice so radical?

3. Elevate Emotion and Embodiment

Data science is weighed down by the false binary of reason vs. emotion. As historians, though, we know that there is no such thing as a neutral perspective. Instead, the feminist approach to data science is to embrace emotion and affect as a valid type of data.

4. Rethink Binaries and Hierarchies

False binaries and unjust hierarchies lead to flawed classification systems that overlook or discriminate against certain groups. Problems with classification must be evaluated on a case-by-case basis. Ethical solutions might include adding categories to a classification system, making certain data categories optional, or avoiding gathering some types of data in the first place.

How data is presented is just as important as how it is categorized. Feminist approaches to data visualization, like Amanda Montañez’s infographic on gender and sex in the Scientific American, can challenge false binaries.

5. Embrace Pluralism

Traditional data science focuses on clarity and control, sometimes to the detriment of minoritized voices. Data cleaning is sometimes necessary to prepare data for computational analysis, but it can also enact epistemic violence, perpetuating unjust hierarchies by separating data from their context.

Feminist data scientists, on the other hand, embrace multiple perspectives. Focusing on team projects and community-driven work can give us better, more complete information than the work of a single individual.

What does embracing pluralism look like in digital/public history? What are the benefits? The challenges? Are there any situations in which we should reject pluralism?

6. Consider Context

Data is meaningless without context. In this chapter, D’Ignazio and Klein coin the term Big Dick Data to refer to “big data projects that are characterized by masculinist, totalizing fantasies of world domination as enacted through data capture and analysis” (151). Big Dick Data projects overstate their scope and importance and ignore essential context. These inaccuracies can in turn lead to massively erroneous reporting, like in this FiveThirtyEight article on kidnappings in Nigeria.

Data are never raw. They are inherently cooked by their sociopolitical and historical context, and that context is essential to accurate data collection, interpretation, and visualization. Institutions need to invest significant funding into documenting, restoring, and communicating context, especially in instances involving discrimination and inequity.

What might “big dick history” look like? Can you think of any examples?

7. Make Labor Visible

Much of the effort goes into data science is invisible labor, paid, underpaid, and unpaid. Data feminism requires that we make labor visible and always give credit where credit is due.

What are some ways labor can be hidden in academia and public history? How do we rectify this?


D’Ignazio and Klein’s data textbook is built on a foundation of Black feminism, an intersectional ideology that prioritizes humanity and process over profit. This is a great and easy intro into data science for humanities scholars and into feminist thought for data scientists. It’s a long read, but well worth the journey. In class, we’ll think about how we can apply these principles to digital history projects. Happy reading!