Gig Work at the Margins: Algorithmic Control and Human Dignity

Despite significant growth, gig platforms have fallen short of alleviating marginalization as many workers, especially marginalized ones, face deteriorating working conditions including losing promised autonomy, experiencing unfair treatment, and earning below minimum wage, all indicating dignity loss. A key contributor is the prevalent use of algorithmic control. While some workers value algorithmic suggestions and rewards that enhance their contributions and earnings, others complain about unrealistic delivery times and misleading price surges. This study examines the dual effects of algorithmic control on human dignity, focusing on its variations by race, ethnicity, immigration status, and income level. Grounded in algorithmic control literature and CARE theory, this study posits that the dual impact of algorithmic control on human dignity depends on how people are treated through different control mechanisms. A survey will collect responses from gig workers across different demographic characteristics to test the theoretical model. This study will advance algorithmic management and control research by offering insights into its duality and its impact on marginalization. This study will also enrich human dignity research by broadening its scope beyond autonomy. This research will offer actionable insights for platform designers and policymakers to create more equitable and supportive work environments.