text: 4 UNFATHOMABLE TRAINING DATA 
  The size of data available on the web has enabled deep learning 
  models to achieve high accuracy on specific benchmarks in NLP 
  and computer vision applications. However, in both application 
  areas, the training data has been shown to have problematic charac- 
  teristics [18, 38, 42, 47, 61] resulting in models that encode stereo- 
  typical and derogatory associations along gender, race, ethnicity, 
  and disability status [11, 12, 69, 69, 132, 132, 157]. In this section, 
  we discuss how large, uncurated, Internet-based datasets encode 
  the dominant/hegemonic view, which further harms people at the 
  margins, and recommend significant resource allocation towards 
  dataset curation and documentation practices. 
  
  4.1 Size Doesn’t Guarantee Diversity 
  The Internet is a large and diverse virtual space, and accordingly, it 
  is easy to imagine that very large datasets, such as Common Crawl 
  (“petabytes of data collected over 8 years of web crawling”,11 a 
  filtered version of which is included in the GPT-3 training data) must 
  therefore be broadly representative of the ways in which different 
  people view the world. However, on closer examination, we find that 
  there are several factors which narrow Internet participation, the 