text: If the LM or word embeddings derived from it are used as com- 
  ponents in a text classification system, these biases can lead to 
  allocational and/or reputational harms, as biases in the representa- 
  tions affect system decisions [125]. This case is especially pernicious 
  for being largely invisible to both the direct user of the system and 
  any indirect stakeholders about whom decisions are being made. 
  Similarly, biases in an LM used in query expansion could influence 
  search results, further exacerbating the risk of harms of the type 
  documented by Noble in [94], where the juxtaposition of search 
  queries and search results, when connected by negative stereotypes, 
  reinforce those stereotypes and cause psychological harm. 
  
  The above cases involve risks that could arise when LMs are de- 
  ployed without malicious intent. A third category of risk involves 
  bad actors taking advantage of the ability of large LMs to produce 
  large quantities of seemingly coherent texts on specific topics on 
  demand in cases where those deploying the LM have no investment 
  in the truth of the generated text. These include prosaic cases, such 
  as services set up to ‘automatically’ write term papers or interact on 
  social media,23 as well as use cases connected to promoting extrem- 
  ism. For example, McGuffie and Newhouse [80] show how GPT-3 
  could be used to generate text in the persona of a conspiracy theo- 
  rist, which in turn could be used to populate extremist recruitment 
  message boards. This would give such groups a cheap way to boost 
  recruitment by making human targets feel like they were among 
  many like-minded people. If the LMs are deployed in this way to 
  recruit more people to extremist causes, then harms, in the first 
  instance, befall the people so recruited and (likely more severely) 
  to others as a result of violence carried out by the extremists. 