A Task-Interdependency Model of Complex Collaborative Work for Advancing Human-Centered Crowd Work

Abstract

Models of crowdsourcing and human computation often assume that individuals independently carry out small, modular tasks. However, while these models have successfully shown how crowds can accomplish significant objectives, they can inadvertently advance a less than human view of crowd workers and fail to capture the unique human capacity for complex collaborative work. We present a model centered on interdependencies—a phenomenon well understood to be at the core of collaboration—that allows one to formally reason about diverse challenges to complex collaboration. Our model represents tasks as an interdependent collection of subtasks, formalized as a task graph. We use it to explain challenges to scaling complex collaborative work, underscore the importance of expert workers, reveal critical factors for situated learning, and explore the relationship between coordination intensity and occupational wages. Using data from O*NET and the Bureau of Labor Statistics, we introduce an index of occupational coordination intensity to validate our theoretical predictions and present preliminary evidence that occupations with greater coordination intensity are less exposed to displacement by AI. We conclude by discussing implications for a crowdsourcing compiler and point to opportunities for models that emphasize the collaborative capacities of human workers, that consider how to support learning, and that bridge models of crowd work and traditional work.

Keywords: human computation and crowdsourcing, complex collaborative work, situated learning, humans and AI, task-interdependency networks, coordination intensity

JEL Classification: D26, J21, J23, J24, J31, J68