text: discussions which will be included via the crawling methodology, 
  and finally the texts likely to be contained after the crawled data 
  are filtered. In all cases, the voices of people most likely to hew to 
  a hegemonic viewpoint are also more likely to be retained. In the 
  case of US and UK English, this means that white supremacist and 
  misogynistic, ageist, etc. views are overrepresented in the training 
  data, not only exceeding their prevalence in the general population 
  but also setting up models trained on these datasets to further 
  amplify biases and harms. 
  
  Starting with who is contributing to these Internet text collec- 
  tions, we see that Internet access itself is not evenly distributed, 
  resulting in Internet data overrepresenting younger users and those 
  from developed countries [100, 143].12 However, it’s not just the In- 
  ternet as a whole that is in question, but rather specific subsamples 
  of it. For instance, GPT-2’s training data is sourced by scraping out- 
  bound links from Reddit, and Pew Internet Research’s 2016 survey 
  reveals 67% of Reddit users in the United States are men, and 64% 
  between ages 18 and 29.13 Similarly, recent surveys of Wikipedians 
  find that only 8.8–15% are women or girls [9]. 