A Task-Interdependency Model of Complex Collaboration Towards Human-Centered Crowd Work

TL;DR

This paper introduces a formal model of complex human collaboration in crowd work based on task interdependencies, highlighting challenges, the role of expertise, and the impact of coordination on job stability amid AI advancements.

Contribution

It presents a novel task graph model for complex collaboration, linking coordination intensity with occupational stability and providing empirical validation using labor data.

Findings

Higher coordination intensity correlates with lower AI displacement risk.

The model explains challenges in scaling complex collaborative tasks.

Occupations with more interdependent tasks require more expert workers.

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 learning on the job, 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. We present preliminary evidence that occupations with greater coordination intensity are less exposed to displacement by AI, and discuss opportunities for models that emphasize the collaborative capacities of human workers, bridge models of crowd work and traditional work, and promote AI in roles augmenting human collaboration.