text: by drastic floods7 pay the environmental price of training and 
  deploying ever larger English LMs, when similar large-scale models 
  aren’t being produced for Dhivehi or Sudanese Arabic?8 
  
  And, while some language technology is genuinely designed to 
  benefit marginalized communities [17, 101], most language technol- 
  ogy is built to serve the needs of those who already have the most 
  privilege in society. Consider, for example, who is likely to both 
  have the financial resources to purchase a Google Home, Amazon 
  Alexa or an Apple device with Siri installed and comfortably speak 
  a variety of a language which they are prepared to handle. Fur- 
  thermore, when large LMs encode and reinforce hegemonic biases 
  (see §§4 and 6), the harms that follow are most likely to fall on 
  marginalized populations who, even in rich nations, are most likely 
  to experience environmental racism [10, 104]. 
  
  These models are being developed at a time when unprece- 
  dented environmental changes are being witnessed around the 
  world. From monsoons caused by changes in rainfall patterns due 
  to climate change affecting more than 8 million people in India,9 
  to the worst fire season on record in Australia killing or displacing 
  nearly three billion animals and at least 400 people,10 the effect 
  of climate change continues to set new records every year. It is 
  past time for researchers to prioritize energy efficiency and cost 
  to reduce negative environmental impact and inequitable access 
  to resources — both of which disproportionately affect people who 
  are already in marginalized positions. 