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Towards fair and pro-social employment of digital pieceworkers for sourcing machine learning training data

Abstract:

This 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 of eforts 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.