Seeing like a driver: How workers repair, resist, and reinforce the platform's algorithmic visions
Name
20539517221133780.pdf
Size
5.08 MB
Format
Adobe PDF
Checksum (MD5)
ea6d8b4b658cbe6fb330b304efdcc5d7
Author(s)
Qadri, Rida
D’Ignazio, Catherine
Date Issued
July 2022
Publisher
SAGE Publications
Citation
Qadri, Rida and D’Ignazio, Catherine. 2022. "Seeing like a driver: How workers repair, resist, and reinforce the platform's algorithmic visions." 9 (2).
Version
Final published version
Abstract
This article theorizes the relationship between two ways of “seeing” and organizing urban mobility markets: the abstract, algorithmic vision of the mobility platform and the experiential, relational vision of the platform driver. Using the case of mobility platforms in Jakarta, we empirically demonstrate how drivers experience the limitations of the platform's visions and how they deploy their own alternative visions of work and the city. We offer this drivers’ “View from Within” as a counterpoint to the visions of the platform, decentering the platform's visions as the sole arbiter of change and optimization in the city. At the same time, we disrupt the assumed binary between these views, showing how they exist in a complex dance of complementarity and contestation. We conclude with a discussion on the opportunities this entanglement presents for worker agency in the algorithmic market, the hurdles toward more “worker centered design” in platform economies and the tensions between globalizing technological solutions and their localized instantiations. Through this article, we argue for seeing deep, embedded relationships as culturally and historically important modes of urban life which technology has to interact with but cannot fully capture nor do away with.
Subjects
Library and Information Sciences
Information Systems and Management
Computer Science Applications
Communication
Information Systems
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/20539517221133780
