Honorable Mention
Prior research has studied the detrimental impact of algorithmic management on gig workers and strategies that workers devise in response. However, little work has investigated alternative platform designs to promote worker well-being, particularly from workers' own perspectives. We use a participatory design approach wherein workers explore their algorithmic imaginaries to co-design interventions that center their lived experiences, preferences, and well-being in algorithmic management. Our interview and participatory design sessions highlight how various design dimensions of algorithmic management, including information asymmetries and unfair, manipulative incentives, hurt worker well-being. Workers generate designs to address these issues while considering competing interests of the platforms, customers, and themselves, such as information translucency, incentives co-configured by workers and platforms, worker-centered data-driven insights for well-being, and collective driver data sharing. Our work offers a case study that responds to a call for designing worker-centered digital work and contributes to emerging literature on algorithmic work.
https://dl.acm.org/doi/abs/10.1145/3491102.3501866
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)