NSF Award
2326378
This Future of Work at the Human-Technology Frontier - Research: Medium (FW-HTF-RM) award supports research to study and mitigate the growing inequality between platforms and workers in digitally-mediated gig work caused by artificial intelligence (AI). The project specifically targets app-based ridesharing, a newly emerging industry with more than 1.5 million drivers in the United States as of 2023. In ridesharing, concerns of inequality such as income disparities and workplace discrimination are frequently observed and reported. This emerging AI inequality is driven by two facets: a technology divide and a data divide. The technology divide pertains to how gig work platforms use advanced AI systems to allocate resources, dispatch tasks, and determine worker pay, while workers lack comparable technological access. The data divide refers to the platforms' collection and consolidation of vast data from all workers and customers to aid their operations, while other parties remain without similar data access. The project will first measure and characterize such AI inequality in rideshare platforms. Based on the derived insights, the research team will design, create, and deploy an AI-enabled data-driven decision-making support system for drivers to help them plan for their work in their best interests, with a long-term goal of bridging AI inequality in rideshare platforms. Outcomes from this project will also benefit other domains of on-demand gig work with algorithmic management, such as online freelancing and data annotation.<br/><br/>This project brings together several disciplines, including human-computer interaction, machine learning, labor economics, and sociology of labor. The investigator team is structured to achieve multiple convergent goals. First, the project seeks to quantitatively measure AI inequality between platforms and workers using a data-driven approach. Second, the project will characterize AI inequality and how drivers react to algorithmic management using a mix of qualitative and quantitative methods from a sociological perspective. Utilizing the findings, the research team will develop a bottom-up network of intelligent personal assistants that help drivers plan for their work and make decisions in their best interests. A network of drivers and their assistants share data, which enables the predictive modeling of task demand and supply, customer and worker behaviors, and pricing changes. Lastly, through a field deployment, the research team will study the adoption of the researched system and measure its real-world impacts. This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human- technology partnership in work contexts by advancing design of intelligent work technologies that operate in harmony with human workers.<br/><br/>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.
