Nets of Fairness. Graph-Based Inference and Visualization to Delve into Gig Workers’ Conditions

The paper explores the integration of graph-based inferences and visualizations to feed novel, experimental approaches to the critical exploration of gig economy workers’ conditions. The analysis builds upon an ongoing research project that has already turned into the design of GigAdvisor, a crossplatform (web and mobile) application for collecting and displaying worker evaluations of digital labor platforms. After a brief introduction to the project, we explore how networks can serve as tools for knowledge discovery and civic engagement: how, on the one hand, they allow us to uncover relational patterns and discover structural dynamics often concealed in tabular or numerical formats and how, on the other, they provide new, intuitive ways to reflect on platform labor and its regulation. The paper outlines the theoretical underpinnings and design choices underlying the approach and illustrates a key use case drawn from the food delivery sector. The concluding section summarizes the implications of our approach, sketching future research directions with a special focus on the integration of graph neural networks in the discovery process.