Navigating Multi-Stakeholder Incentives and Preferences: Co-Designing Alternatives for the Future of Gig Worker Well-Being
arXiv:2302.13436 · doi:10.1145/3563657.3595982
Abstract
Gig workers, and the products and services they provide, play an increasingly ubiquitous role in our daily lives. But despite growing evidence suggesting that worker well-being in gig economy platforms have become significant societal problems, few studies have investigated possible solutions. We take a stride in this direction by engaging workers, platform employees, and local regulators in a series of speed dating workshops using storyboards based on real-life situations to rapidly elicit stakeholder preferences for addressing financial, physical, and social issues related to worker well-being. Our results reveal that existing public and platformic infrastructures fall short in providing workers with resources needed to perform gigs, surfacing a need for multi-platform collaborations, technological innovations, as well as changes in regulations, labor laws, and the public's perception of gig workers, among others. Drawing from multi-stakeholder findings, we discuss these implications for technology, policy, and service as well as avenues for collaboration.
References in corpus (4)
- Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
- Disrupting the Coming Robot Stampedes: Designing Resilient Information Ecologies
- SEPAR: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees
- A Little Too Personal: Effects of Standardization versus Personalization on Job Acquisition, Work Completion, and Revenue for Online Freelancers
Cited by in corpus (4)
- DISCERN: Designing Decision Support Interfaces to Investigate the Complexities of Workplace Social Decision-Making With Line Managers
- Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work
- Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling
- Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequalities