Full project title:
Gendered Experiences of AI-mediated work: E-hailing and Delivery drivers in the United Kingdom
Extensive literature has documented the dangerous and precarious working conditions associated with algorithmically managed gig economy work where large-scale platforms use Artificial Intelligence (AI) to allocate work and determine wages in ways that are opaque beyond corporate offices. Nonetheless, gig work platforms across delivery, domestic and online services offer income opportunities to millions across the globe who have come to depend on gig work as their main income source.
Early excitement around the potential of gig work for women who are associated with needing ‘flexible’ working conditions have been replaced with the reality that gender minorities face significant, and specific, inequalities in gig work. This includes wage differentials, the effect of gender perception on the algorithmic allocation of work and violence and harassment from consumers and other drivers.
Without understanding the specific of gendered experiences on the app, preliminary research shows these gender-blind algorithms mediate work in ways that disadvantage women and gender minorities, further exacerbated by platform affordances that do not respond to the lived experiences of workers.
Qualitative interviews with women and gender minority gig workers and field experts will establish common safety risks associated with e-hailing and delivery work, along with examining existing informal risk-mitigation tactics employed by workers in response to algorithmic management and platform design. The interview data will then use speculative design methods to identify and prototype improvement that can be made to platform affordances and algorithmic management systems.