Guest lecture: Hatim Rahman
Ehrlicher Room, 3100 North Quad
From Iron Cages to Invisible Cages: Algorithmic evaluations in online labor markets
Existing information systems and organizational theory suggests that algorithms and artificial intelligence are “tightening” the iron cage; these systems represent the next frontier of rationalization, controlling the way people work at scale. This paper, however, reveals how organizations’ use of algorithms is creating modern day invisible cages, in which platforms deliberately hide the norms and expectations for how people should behave. Specifically, I examined how a platform organization transitioned from a rating algorithm using transparent evaluation criteria to a new opaque algorithm evaluating and stratifying workers.
Drawing on interviews, archival data, and participant observation as a registered user on the platform, I show that the platform strategically used its algorithm to manufacture uncertainty amongst workers, leading to “superstitious reactivity”: a form of reactivity to algorithmic evaluations that instigates divergent practices rather than convergence. I found that even highly-rated workers, who were one of the primary intended beneficiaries of the algorithmic reform, responded to the opaque algorithmic evaluation by changing how they used the platform in ways the platform could not control. Theoretically, this paper reveals how organizations can use algorithms to implement apparently rational systems, increasingly driven by obscurity and uncertainty rather than transparency and accountability. I discuss the implications my findings have for the literature on information systems, algorithms, and work in the emerging economy.
About the speaker:
Hatim A. Rahman is a PhD candidate at Stanford’s Work, Technology, and Organization (WTO) program in the Department of Management Science and Engineering. His research investigates how algorithms and other forms of artificial intelligence are shaping the future of work, including for people from underrepresented groups. In particular, he studies how diverse people contend with the torrent of changes they encounter in the emerging digital economy.
He uses both qualitative field methods and computational social science techniques in his research. His recent dissertation paper uses field data collected through participant observation, interviews, and archival sources to study how sophisticated algorithms are being used by digital platform organizations (e.g., Upwork, Uber, TaskRabbit) to disrupt the way people work and are evaluated in online labor markets. His research has received several awards, including being selected as a finalist for the 2017 INFORMS Dissertation Competition. His research has also been selected as a finalist for the 2017 “Best Student Paper” award and as a “Runner Up” for the same award at the 2018 Academy of Management Conference in the Organizational Communication and Information Systems division (OCIS).