CAREER: Enabling Computer Supported Collective Action Systems for Gig Knowledge Workers

Northeastern University, Boston MA

Investigators

Abstract

A number of platforms match workers with employers offering short tasks or jobs that require some knowledge, such as writing short articles, editing videos, or labeling data for artificial intelligence (AI) algorithms. This "gig knowledge work" provides workers useful flexibility, but also a number of challenges, such as limited opportunities for mentoring and apprenticeship, lower wages than they might make elsewhere, and/or potential wage theft by employers who reject valid work. This project's goal is to design new tools to help gig knowledge workers identify shared challenges and collaborate to address them. These tools will allow workers to collect and make sense of job, wage, and employer data as a group in order to identify challenges they face together, and to develop and implement plans for taking action around those challenges. Through this research, the project team will advance understanding on both the challenges gig knowledge workers face and ways to address them, as well as around developing tools to support collective action more generally. The findings and tools will be disseminated widely through computer science courses for high school and college students that encourage socially responsible and nuanced design, as well as through workshops aimed at gig knowledge workers themselves. The research is organized around human-centered engineering approaches that address three main challenges: collecting data on gig labor, interpreting this data, and enabling collective action based on the insights gained. For data collection, the research team will work closely with gig knowledge workers to design monitoring tools that enable the aggregation of job-related data without adding to their workload or risking violations of platform terms of service. For understanding, the team will create and evaluate tools that analyze the collected data, grouping workers based on similar work activities and challenges. These tools will also incorporate suggestions from large language models to aid in brainstorming solutions for the identified challenges. For collective action, the team will create systems that support workers in implementing the plans devised during the brainstorming sessions, ensuring the plans are carried out in a socially cohesive and accountable manner. In all three sub-projects, the team will adopt a participatory design approach, guided by social science theories of collective action and social identity and leveraging capabilities of generative AI technologies to create user-friendly, intelligent interfaces that are specifically tailored for the needs of gig knowledge workers. To study and evaluate these technologies, the research team will conduct field experiments and longitudinal deployments in the real world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.