Session 13

Data feminism

Author
Affiliation

Tobias Hodel

Walter Benjamin Kolleg / Digital Humanities, University of Bern

Published

8 December 2026

Modified

2 September 2026

Hodel Taught by Tobias Hodel.

In this session

  • Discussion of (Bender et al. 2021)
  • Data feminism as a framework
  • Reviewing the semester from an intersectional perspective

Data feminism

Following D’Ignazio and Klein (D’Ignazio and Klein 2020) and Lang and Suárez Cronauer (Lang and Suárez Cronauer 2026):

  • Examine power. Who made this dataset, who benefits from it, who is counted in it?
  • Challenge power. Data work as a site of intervention, not only description.
  • Elevate emotion and embodiment. Against the fiction of the view from nowhere.
  • Rethink binaries and hierarchies. Categories are decisions, and they exclude.
  • Embrace pluralism. Many partial perspectives beat one claimed-universal one.
  • Consider context. Data is never raw; decontextualised data is misleading data.
  • Make labour visible. Whose invisible work made this dataset possible?

Reviewing the semester

We go back over every session and ask the same set of questions:

Session The question
What is DH? Whose definition of the field prevailed, and who was not in the room?
Data and metadata What did the standard field list make unsayable?
Data structures Which categories did your model force, and who did they fit badly?
Linked Open Data Whose entities have Wikidata identifiers, and whose do not?
Publication Who can actually read what you published — technically, linguistically, financially?
Editions Which texts get edited, and which have never been thought worth it?
Networks Who ends up at the centre of a network, and is that a finding or an artefact of the source?
Text analysis Whose language is the model, the stopword list and the tokenizer built for?
Geovisualisation Whose place names are on the base map?
Machine learning Who labelled the data, and under what conditions?
LLMs Whose text was scraped, and who pays the environmental cost?

Bring your own project to this. The most useful version of this session is the one where you find something uncomfortable in your own work.

TipIn parallel in the Lab

The DH Lab has workflow presentations this week.

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References

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. ‘On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?’ Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (New York, NY, USA), March, 610–23. https://doi.org/10.1145/3442188.3445922.
D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. The MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001.
Lang, Sarah, and Elena Suárez Cronauer. 2026. ‘Beyond Data Feminism. Towards Ethical Data Work in the (Digital) Humanities’. Zeitschrift für Digitale Geisteswissenschaften, ahead of print, February. https://doi.org/10.17175/wp_2026.