Bargaining with the Black-Box: Designing and Deploying Worker-Centric Tools to Audit Algorithmic Management

Dan Calacci, Alex Pentland

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

The increasing prevalence of large-scale labor aggregation platforms, worker analytics, and algorithmic decision-making by management raises the question of whether workers can use similar technologies to advocate for their own goals. Yet, there are inherent challenges in building worker-centric tools that collect, aggregate, and share data in responsible and ethical ways. In this paper, we present the design and deployment of the Shipt Calculator, a tool developed in collaboration with non-profit worker groups that allows app-based delivery workers to track and share aggregate data about their pay, increasing wage transparency. We first discuss the design challenges inherent to building worker-centric technologies, particularly for informally organized workers, and ground our discussion in the history of worker inquiry and co-research. We then describe some principles from this history and our own lessons in designing the Calculator that can be applied by future researchers and advocates seeking to build technical tools for organizing campaigns. Finally, we share the results of using the Calculator to audit an app's shift to a black-box pay model using data contributed by 140 workers in the Summer of 2020, finding that although the average pay per-order increased under the new payment model, almost half of workers experienced an unannounced pay cut during the shift, and many workers worked shifts that paid under their state's minimum wage. Finally, we discuss how tools like the Calculator demonstrate the important role that aggregate worker data, and a new Digital Workerism, can serve in creating and maintaining a more balanced platform economy.

Original languageEnglish (US)
Article number3570601
JournalProceedings of the ACM on Human-Computer Interaction
Volume6
Issue number2 CSCW
DOIs
StatePublished - Nov 11 2022

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Human-Computer Interaction
  • Computer Networks and Communications

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