September 28, 2022

Women who work for apps like Uber and Doordash often ‘push off’ harassment

Questions and answers

Gig industry platforms like Uber, Doordash and TaskRabbit fail to recognize the realities of female workers’ experiences, putting women at financial and personal risk, according to a new study.

Researchers interviewed 20 female gig workers in Canada and the United States and found that women – who make up about half of the gig workforce in Canada – often have to ‘speak up’ about harassment for fear of losing. their work, and the security tools available are not very effective. . Authors Dr. Ning Ma (she she), a postdoctoral fellow, and Dr. Dongwook Yoon (he she), assistant professor, from UBC’s computer science department, discuss what small businesses can do to improve the worker experience and reduce risk.

What are the realities of the experiences of gig workers?

MN: Female gig workers who took part in our study believe they faced a disproportionate amount of harassment and prejudice, with incidents such as people following delivery workers to their homes to yell at them, and men refusing to leave the vehicles. We identified that these issues stem from gender-neutral designs on the platform – designs that ignore gendered experiences. For example, platforms do not have clear policies to help female drivers set clear boundaries when interacting with drivers: when is it okay to kick someone out of the car without being penalized by a bad rating from this driver?

DY: We also identified the unique values ​​that female workers bring to the platform. For example, female passengers often feel safer with female drivers, which also adds to the perceived safety of the platform as a whole. However, gig platform dispatch algorithms do not recognize or reward these values, including sending gig workers to female clients to improve their access to work and potentially avoid harassment.

Conversely, gigs in traditionally male-dominated fields, such as furniture assembly or heavy lifting, often pay better. Female workers who had previous experience in male-dominated environments felt more comfortable taking on these tasks, which translated into higher pay and felt more secure in these environments.

Why are current anti-harassment measures not working?

MN: The most common security measure is an emergency button that triggers a call to 9-1-1. The vast majority of harassment does not escalate to this level but is often much more subtle, for example inviting women into clients’ homes or inappropriate verbal requests. Calling 9-1-1 wastes time for workers, who have to get to the next commute or the next food order to earn money. Time is of the essence in this type of work, which has largely led women to “dismiss” harassment. Subtle harassment is always unacceptable, and there needs to be an interim step to support female gig workers.

What can companies do to improve things?

DY: Platforms should treat gig workers as legitimate stakeholders in platform design, rather than exploiting them as vehicles to market their equity and inclusion programs. At the macro level, this means paying attention to the experiences and challenges of female workers when designing the platform’s functionality and workflow. Specifically, this could take the form of having clear guidelines on how workers can handle customer interaction, for example, when is it acceptable to stop service, and signs posted in vehicles. describing this policy. Additionally, platforms could allow drivers to give context to negative ratings and feed them into attribution algorithms.

MN: Platforms could also allow customers to connect with female workers at certain times of the day, such as late at night. These platforms are a new form of workplace, and they need to start looking at gig workers as “workers” and investing in their safety and comfort.

This researchco-authored with Veronica Rivera, PhD student at the University of California, Santa Cruz, and Zheng Yao, PhD student at Carnegie Mellon University, will be presented at the ACM Conference on Human Factors in Computing Systems (CHI) 2022.

Language(s): English (Yoon, Ma)