text: These demonstrations of biases learned by LMs are extremely 
  valuable in pointing out the potential for harm when such models 
  are deployed, either in generating text or as components of classi- 
  fication systems, as explored further in §6. However, they do not 
  represent a methodology that can be used to exhaustively discover 
  all such risks, for several reasons. 
  
  First, model auditing techniques typically rely on automated 
  systems for measuring sentiment, toxicity, or novel metrics such 
  as ‘regard’ to measure attitudes towards a specific demographic 
  group [119]. But these systems themselves may not be reliable 