How do we rate each other? - A study of Uber's rider-driver rating perceptions in India

Shriram Venkatraman, Nalin Gupta

Research output: Contribution to conference without publisher/journalPaperResearchpeer-review


As Uber's stake in India, particularly in the metros, is on the rise, it becomes important for this ridesharing company to maintain the quality of service, trust, and reliability when moving people around the city space. To ensure this while also maintaining employee satisfaction, Uber tends to follow the two-sided rating system where both the rider and the driver judge and rate each other after every trip.

Though popularly advertised by Uber as a trust-building exercise to increase cooperation and respect between the rider and the driver, such rating through algorithmic design also has an impact on the driver-rider matching when ordering a cab service, thus in a way affecting the riding experience and the value for money. The system has several unintended consequences in a socio-cultural context such as India and very specifically for the middle class who use this service often. The pressure to maintain a certain high score very often gets juxtaposed with the burden to present themselves and constantly judge each other in ways that sometimes increase rater bias.

This paper using both ethnography from an anthropological perspective and computational methods intends to show how the riders and the drivers perceive and rate each other and how such perceptions may lead to rating collusions and sometimes in collisions.
Original languageEnglish
Publication date2018
Publication statusPublished - 2018
Externally publishedYes
EventASA18: Sociality, matter, and the imagination: re-creating Anthropology - Examination Schools, University of Oxford, United Kingdom
Duration: 18. Sept 201821. Sept 2018


LocationExamination Schools, University of Oxford
Country/TerritoryUnited Kingdom

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