text: 6 STOCHASTIC PARROTS 
  In this section, we explore the ways in which the factors laid out in 
  §4 and §5 — the tendency of training data ingested from the Internet 
  to encode hegemonic worldviews, the tendency of LMs to amplify 
  biases and other issues in the training data, and the tendency of re- 
  searchers and other people to mistake LM-driven performance gains 
  for actual natural language understanding — present real-world 
  risks of harm, as these technologies are deployed. After exploring 
  some reasons why humans mistake LM output for meaningful text, 
  we turn to the risks and harms from deploying such a model at 
  scale. We find that the mix of human biases and seemingly coherent 
  language heightens the potential for automation bias, deliberate 
  misuse, and amplification of a hegemonic worldview. We focus 
  primarily on cases where LMs are used in generating text, but we 
  will also touch on risks that arise when LMs or word embeddings 
  derived from them are components of systems for classification, 
  query expansion, or other tasks, or when users can query LMs for 
  information memorized from their training data. 