Toward a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit

Ridesharing PlatformsAlgorithmic Fairness & BiasGovernment Officials & Civil ServantsStatisticians & Data Scientists

Despite the geographically situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography—distance decay, structured variation in population density, mental maps, and “the Big Sort” (spatial homophily)—manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.

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https://hci.top/en/papers/chi/4025/2018

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Source
CHI
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Year
2018
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3 authors
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Subtopics
Ridesharing Platforms, Algorithmic Fairness & Bias
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Government Officials & Civil Servants, Statisticians & Data Scientists
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Abstract only
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