text: While [129] benchmarks the training process in a research set- 
  ting, many LMs are deployed in industrial or other settings where 
  the cost of inference might greatly outweigh that of training in 
  the long run. In this scenario, it may be more appropriate to de- 
  ploy models with lower energy costs during inference even if their 
  training costs are high. In addition to benchmarking tools, works 
  estimating the cost increase associated with the introduction of LMs 
  for particular applications, and how they compare to alternative 
  NLP methods, will be important for understanding the trade-offs. 
  When we perform risk/benefit analyses of language technology, 
  we must keep in mind how the risks and benefits are distributed, 
  because they do not accrue to the same people. On the one hand, it 
  is well documented in the literature on environmental racism that 
  the negative effects of climate change are reaching and impacting 
  the world’s most marginalized communities first [1, 27].6 Is it fair or 
  just to ask, for example, that the residents of the Maldives (likely to 
  be underwater by 2100 [6]) or the 800,000 people in Sudan affected 