@inproceedings{1006,
  author = {Annabel Rothschild and Justin Booker and Christa Davoll and Jessica Hill and Venise Ivey and Ben Rydal Shapiro and Betsy DiSalvo},
  title = {Towards fair and pro-social employment of digital pieceworkers for sourcing machine learning training data},
  abstract = {This work contributes to just and pro-social treatment of digital pieceworkers ("crowd collaborators") by reforming the handling of crowd-sourced labor in academic venues. With the rise in automation, crowd collaborators' treatment requires special consideration, as the system often dehumanizes crowd collaborators as components of the “crowd” [41]. Building off efforts to (proxy-)unionize crowd workers and facilitate employment protections on digital piecework platforms, we focus on employers: academic requesters sourcing machine learning (ML) training data. We propose a cover sheet to accompany submission of work that engages crowd collaborators for sourcing (or labeling) ML training data. The guidelines are based on existing calls from worker organizations (e.g., Dynamo [28]); professional data workers in an alternative digital piecework organization; and lived experience as requesters and workers on digital piecework platforms. We seek feedback on the cover sheet from the ACM community},
  year = {2022},
  journal = {CHI Conference on Human Factors in Computing Systems Extended Abstracts},
  month = {01},
  url = {https://par.nsf.gov/biblio/10357237},
  doi = {10.1145/3491101.3516384},
}