Data Feminism

CTL-E-6365 · Work · book · 2020–present

VERIFIED evidence grade — build store (JSON graph)

Combined Graph record U-07551 · provenance: build store (JSON graph), record CTL-E-6365.

Data Feminism is a 2020 book by Catherine D'Ignazio and Lauren F. Klein, published by MIT Press. It argues that data science should be informed by intersectional feminist thought, and examines data science as power — a force that can be used to expose injustice and challenge inequality, and that can also discriminate, police, and surveil. The book is organized around seven principles, stated in its own text: examine power; challenge power; elevate emotion and embodiment; rethink binaries and hierarchies; embrace pluralism; consider context; and make labor visible. An open-access edition, funded by the MIT Libraries, is published free online by MIT Press, making the book itself an instance of the access politics it advocates. The principles are taken here from the book's own open-access site, whose wording this record follows over secondary paraphrase.

Notes

STAGING: temp id EB-21. The authored_by edge to co-author Lauren F. Klein is BLOCKED as target-absent (Klein has no live record and is not one of the 21) — see blocked edges; only the D'Ignazio edge is emitted. No reception/prize claims are made: none were verified in this pass beyond praise quoted by the publisher itself. CURATOR'S JUDGMENT (DRAFT): The packet closes where it should: a method for doing data work justly, published open-access by its own authors.

Relationships

• Catherine D'Ignazio — authored by
  Data Feminism (MIT Press, 2020) is co-authored by Catherine D'Ignazio and Lauren F. Klein; the D'Ignazio edge is emitted here and the Klein edge is target-absent (see blocked edges).

Sources

• MIT Press (accessed 2026-10-29): Data Feminism, Catherine D'Ignazio and Lauren F. Klein, MIT Press, 2020 (hardcover ISBN 9780262044004; a 2023 paperback, ISBN 9780262547185, also exists — 2023 must not overwrite 2020 as the publication date). Data science informed by intersectional feminist thought; data science as power — used to expose injustice and to discriminate, police, surveil. (primary source · build store)
• Data Feminism OA site, Introduction (accessed 2026-10-29): 1 Examine power · 2 Challenge power · 3 Elevate emotion and embodiment · 4 Rethink binaries and hierarchies · 5 Embrace pluralism · 6 Consider context · 7 Make labor visible. (Some secondary sources paraphrase principle 3; the book's wording is used.) (primary source · build store)
• Onassis Foundation, authors' infographic page (accessed 2026-10-29): corroborates the seven principles including principle 7, 'Make labor visible.' (build store)

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Record CTL-E-6365 · Combined Graph record U-07551 · CultureTechLens Knowledge Graph · 3 sources · 1 mapped relationship.
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Researched and maintained by the CultureTechLens research program · CultureTechLens Combined Graph, Release 2026-11-04. Corrections are public: the corrections log.

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