text: our research time and effort a valuable resource, to be spent to the 
  extent possible on research projects that build towards a techno- 
  logical ecosystem whose benefits are at least evenly distributed or 
  better accrue to those historically most marginalized. This means 
  considering how research contributions shape the overall direction 
  of the field and keeping alert to directions that limit access. Like- 
  wise, it means considering the financial and environmental costs 
  of model development up front, before deciding on a course of in- 
  vestigation. The resources needed to train and tune state-of-the-art 
  models stand to increase economic inequities unless researchers 
  incorporate energy and compute efficiency in their model evalua- 
  tions. Furthermore, the goals of energy and compute efficient model 
  building and of creating datasets and models where the incorpo- 
  rated biases can be understood both point to careful curation of 
  data. Significant time should be spent on assembling datasets suited 
  for the tasks at hand rather than ingesting massive amounts of data 
  from convenient or easily-scraped Internet sources. As discussed in 
  §4.1, simply turning to massive dataset size as a strategy for being 
  inclusive of diverse viewpoints is doomed to failure. We recall again 
  Birhane and Prabhu’s [18] words (inspired by Ruha Benjamin [15]): 
  “Feeding AI systems on the world’s beauty, ugliness, and cruelty, 
  but expecting it to reflect only the beauty is a fantasy.” 