The Uber Game: Exploring Algorithmic Management and Resistance

Abstract

The algorithmic oversight, optimization and evaluation of worker performance is an increasing reality in many economic sectors. Previous research drawing on interview and forum data has suggested an inequity of power between the operators of an algorithmic management system, and those working under it. This inequity arises through a lack of transparency around the rules that govern their work, and a lack of options for workers to influence those rules in response to the realities of their work. The paper draws on a sample of 9,355 forum threads from a major international Uber driver’s discussion forum using a mix of quantitative and qualitative content analysis using Python and Nvivo CAQDAS. Quantitative techniques such as k-means clustering, term frequency-inverse document frequency (tf-idf) weighting and collocations allowed the exploration of broad patterns such as the primary topics of discussion, and the most significant terms and phrases within those discussions. This paper provides three contributions. First the paper demonstrates the application of natural language processing and contemporary data science tools in the analysis of discourse within particular online spaces. Secondly, the paper draws together literature on algorithmic power, with the field of game studies to provide complimentary insights into the knowledge and power relations between individuals and algorithmic systems. Finally, the paper argues that whilst algorithmic management can impose inequities upon its workers, they are developing strategies for resistance and empowerment, through practices analogous to game-play and rule discovery.