text: Finally, we note that moving beyond demonstrating the exis- 
  tence of bias to building systems that verify the ‘safety’ of some 
  LM (even for a given protected class) requires engaging with the 
  systems of power that lead to the harmful outcomes such a system 
  would seek to prevent [19]. For example, the #MeToo movement has 
  spurred broad-reaching conversations about inappropriate sexual 
  behavior from men in power, as well as men more generally [84]. 
  These conversations challenge behaviors that have been historically 
  considered appropriate or even the fault of women, shifting notions 
  of sexually inappropriate behavior. Any product development that 
  involves operationalizing definitions around such shifting topics 
  into algorithms is necessarily political (whether or not developers 
  choose the path of maintaining the status quo ante). For example, 
  men and women make significantly different assessments of sexual 
  harassment online [40]. An algorithmic definition of what con- 
  stitutes inappropriately sexual communication will inherently be 
  concordant with some views and discordant with others. Thus, an 
  attempt to measure the appropriateness of text generated by LMs, or 
  the biases encoded by a system, always needs to be done in relation 
  to particular social contexts and marginalized perspectives [19]. 