text: As a part of careful data collection practices, researchers must 
  adopt frameworks such as [13, 52, 86] to describe the uses for which 
  their models are suited and benchmark evaluations for a variety of 
  conditions. This involves providing thorough documentation on the 
  data used in model building, including the motivations underlying 
  data selection and collection processes. This documentation should 
  reflect and indicate researchers’ goals, values, and motivations in 
  assembling data and creating a given model. It should also make 
  note of potential users and stakeholders, particularly those that 
  stand to be negatively impacted by model errors or misuse. We note 
  that just because a model might have many different applications 
  doesn’t mean that its developers don’t need to consider stakeholders. 
  An exploration of stakeholders for likely use cases can still be 
  informative around potential risks, even when there is no way to 
  guarantee that all use cases can be explored. 
  
  We also advocate for a re-alignment of research goals: Where 
  much effort has been allocated to making models (and their training 
  data) bigger and to achieving ever higher scores on leaderboards 
  often featuring artificial tasks, we believe there is more to be gained 
  by focusing on understanding how machines are achieving the 
  tasks in question and how they will form part of socio-technical 
  systems. To that end, LM development may benefit from guided 
  evaluation exercises such as pre-mortems [68]. Frequently used in 
  business settings before the deployment of new products or projects, 
  pre-mortem analyses center hypothetical failures and ask team 
  members to reverse engineer previously unanticipated causes.25 
  Critically, pre-mortem analyses prompt team members to consider 
  not only a range of potential known and unknown project risks, but 
  also alternatives to current project plans. In this way, researchers 
  can consider the risks and limitations of their LMs in a guided 
  way while also considering fixes to current designs or alternative 