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Dec 12, 2018
Dec 13, 2018
Dec 13, 2018
Dec 13, 2018


This will help recreate the dataset in the following paper:

Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Adam Kalai. Bias in Bios: A Case Study of Semantic Representation Bias in a High Stakes Setting. Proceedings of FAT*, 2019.

Note: requires python 3 and python packages: warcio (to process the common crawl), pebble (for multiprocessing with timeouts)

Just run ./ and it will download the bios and put them in a python pickled file called BIOS.pkl. Note: the more cores you have on your machine the faster it will be. For example, on a machine with 64 cores, it might take about 6 hours per archive times 16 archives = 4 days.

Further details:

  • takes as an argument an arxiv number and downlaods and extracts the bios into a .pkl file starting with the corresponding CC path.
  • merges all these bios and also creates a version of the bio with names and pronouns scraped.
  • the result is a pickled list of bio records.
  • each bio record is a dictionary
  • r["title"] tells you the noramlized title
  • r["gender"] tells you the gender (binary for simplicity, determined from the pronouns)
  • r["start_pos"] indicates the length of the first sentence.
  • r["raw"] has the entire bio
  • So the classification task is to predict r["title"] from r["raw"][r["start_pos"]:]
  • The field r["bio"] contains a scrubbed version of the bio (with the person's name and obvious gender words (like she/he removed)


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