Algorithmically mediated systems and tools are used by workers across the globe. Many of these workers are in low-power positions, where they have little leverage to make demands around transparency, explanation, or terms of use, yet, at the same time rely deeply on these systems for many aspects of their jobs. This tension between little power and high reliance drives the production of intensive algorithmic imaginaries, where workers engage in meaning-making to construct understandings of these systems. Yet, there has been little attention paid to the diversity and ingenuity of algorithmic understandings crafted by the workers. In this workshop, our goal is to bring together researchers and practitioners from across disciplines to create a research agenda, compare vocabularies, and discuss methodologies around this form of “folk tradecraft.” This toolkit will help elicit insights into these phenomena and ultimately build mechanisms by which the labor of algorithmic meaning-making can be respected, understood, and leveraged for system design.
Citation
Lindsey Cameron, Angele Christin, Michael Ann DeVito, Tawanna R. Dillahunt, Madeleine Elish, Mary Gray, Rida Qadri, Noopur Raval, Melissa Valentine, and Elizabeth Anne Watkins. 2021. “This Seems to Work”: Designing Technological Systems with The Algorithmic Imaginations of Those Who Labor. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Article 115, 1–5. DOI:https://doi.org/10.1145/3411763.3441331
Dr. Michael Ann DeVito, Ph.D. 