Year: 2019
Type: conference-paper
Abstract: It has been suggested that the gig-economy’s elimination of traditional arm’s-length transactions may introduce bias into perceptions of quality. In this work, we build upon research that has identified biases based on ascriptive characteristics in rating systems, and examine gender biases in ridesharing platforms. In doing so, we extend research to consider not simply willingness to transact, but post transaction perceptions of quality. We also examine which types of tasks may yield more biased ratings for female drivers. We find no differences in ratings across gender in the presence of a high quality experience. However, when there is a lower quality experience, penalties for women accrue faster, notably when poorly performed tasks are perceived to be highly gendered.
Authors: Brad Greenwood, Idris Adjerid, Corey Angst
Institutions: University of Notre Dame, University of Minnesota, Virginia Tech
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W2783642693