text: FAccT ’21, March 3–10, 2021, Virtual Event, Canada 
  
  to how it may seem when we observe its output, an LM is a system 
  for haphazardly stitching together sequences of linguistic forms 
  it has observed in its vast training data, according to probabilistic 
  information about how they combine, but without any reference to 
  meaning: a stochastic parrot. 
  
  6.2 Risks and Harms 
  The ersatz fluency and coherence of LMs raises several risks, pre- 
  cisely because humans are prepared to interpret strings belonging 
  to languages they speak as meaningful and corresponding to the 
  communicative intent of some individual or group of individuals 
  who have accountability for what is said. We now turn to examples, 
  laying out the potential follow-on harms. 
  
  The first risks we consider are the risks that follow from the LMs 
  absorbing the hegemonic worldview from their training data. When 
  humans produce language, our utterances reflect our worldviews, 
  including our biases [78, 79]. As people in positions of privilege 
  with respect to a society’s racism, misogyny, ableism, etc., tend 
  to be overrepresented in training data for LMs (as discussed in 
  §4 above), this training data thus includes encoded biases, many 
  already recognized as harmful. 