@inproceedings{8bcdb9819b0642aa8558548e2499c8b5,
title = "{"}Brush it Off{"}: How Women Workers Manage and Cope with Bias and Harassment in Gender-agnostic Gig Platforms",
abstract = "Women make up approximately half of the workforce in ride-hailing, food delivery, and home service platforms in North America. While studies have reported that gig workers face bias, harassment, and a gender pay gap, we have limited understanding of women's perspectives of these issues and their coping mechanisms. We interviewed 20 women gig workers to hear their unique experiences with these challenges. We found that gig platforms are gender-agnostic, meaning they do not acknowledge women's experiences and the value they bring. By not enforcing anti-harassment policies in design, gig platforms also leave women workers vulnerable to bias and harassment. Due to the lack of support for immediate actions and in fear of losing access to work, women workers {"}brush off{"}harassment. In addition, the platforms' dispatching and recommendation mechanisms do not acknowledge women's contributions in perceived safety for customers and social support for peer workers.",
keywords = "Bias, DoorDash, Gender, Gig work, Harassment, Labor, TaskRabbit, Uber, Women",
author = "Ma, \{Ning F.\} and Rivera, \{Veronica A.\} and Zheng Yao and Dongwook Yoon",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 ; Conference date: 30-04-2022 Through 05-05-2022",
year = "2022",
month = apr,
day = "29",
doi = "10.1145/3491102.3517524",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems",
}