Working with Machines

TLDR

Algorithms now manage, assign, optimize, and evaluate distributed human workers across many fields, using data to track performance. This study investigates how algorithmic, data‑driven management affects workers and work practices in Uber and Lyft ridesharing services. Qualitative interviews reveal that drivers adapt to algorithmic assignment, support, and evaluation, and use online forums to interpret algorithm features, with implications for future research.

Abstract

Software algorithms are changing how people work in an ever-growing number of fields, managing distributed human workers at a large scale. In these work settings, human jobs are assigned, optimized, and evaluated through algorithms and tracked data. We explore the impact of this algorithmic, data-driven management on human workers and work practices in the context of Uber and Lyft, new ridesharing services. Our findings from a qualitative study describe how drivers responded when algorithms assigned work, provided informational support, and evaluated their performance, and how drivers used online forums to socially make sense of the algorithm features. Implications and future work are discussed.