text: 4.4 Curation, Documentation & Accountability 
  In summary, LMs trained on large, uncurated, static datasets from 
  the Web encode hegemonic views that are harmful to marginalized 
  populations. We thus emphasize the need to invest significant re- 
  sources into curating and documenting LM training data. In this, 
  we follow Jo et al. [62], who cite archival history data collection 
  methods as an example of the amount of resources that should be 
  dedicated to this process, and Birhane and Prabhu [18], who call for 
  a more justice-oriented data collection methodology. Birhane and 
  Prabhu note, echoing Ruha Benjamin [15], “Feeding AI systems on 
  the world’s beauty, ugliness, and cruelty, but expecting it to reflect 
  only the beauty is a fantasy.” [p.1541] 