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data feminism
eg feminist data set by Caroline Sinders https://carolinesinders.com/feminist-data-set/
STEM work by and for underrepresented minorities
data science as a newer form of power
empirically wrong data categorisation, incorrect and unjust binaries
data visualisation
datasets that are skewed, incomplete or lack diverse representation
- medical studies historically exclude black women
- data collection on trans people very poor
  - i’ve seen a bunch of papers on this i just haven’t got round to cataloguing them here
data witnessing

Mentions

“All data is historical data, the product of a time, place, political, economic, technical and social climate. If you are not considering why your data exists and other datasets don’t, you are doing data science wrong.” — Melissa Terras (quoted in How AI Image Generators Make Bias Worse)

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hidden data flows

Front-end tracking: Cookies, browser fingerprinting, session replay.
Back-end storage: Centralized behavioural databases, AI-driven predictive systems.
Data Layers & Surveillance Markets: Selling behavioural predictions as a commodity
people don’t fund tech anymore if it doesn’t come with extra user data, behavioural analytics