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)

notification

on what is and isn’t urgent
what time and attention you can demand from others
always-contactable mental health toll
work from home monitoring
notifications you can’t switch off, newsletters you didn’t sign up for, spam lists your number gets on
tangible and intangible notifications
- bell that rings when you enter shop
- exact GPS device locations
- data exhaust, signals and mass data collection
- when is data shared, to who and why and how much
as-and-when, asynchronous notification
bulletin boards, check-in methods