A Mathematical Framework for AI-Human Integration in Work

2025-05-29

AI Summary

This study investigates the collaborative relationship between generative AI and human workers, clarifying AI’s role as a capability enhancer—not a replacement—by delineating its functional boundaries. Method: We propose a “decision–execution” dual-layer sub-skill decomposition paradigm, formalizing tasks, workers, and person–job fit as computable models. Calibrated on O*NET and Big-Bench Lite data, we derive a probabilistic success function to quantify productivity gains under AI assistance, identifying a “compression effect” wherein low-skill workers exhibit disproportionate performance uplift. We further establish sufficient conditions for complementary sub-skill synergy across multiple workers. Contribution/Results: Results demonstrate a sharp, non-linear increase in human–AI collaboration success when sub-skills exhibit structural complementarity and task complexity is moderate. The framework provides an interpretable, empirically grounded, and theoretically rigorous foundation for designing AI-augmented work systems.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Philosophy and Ethics of AI: AI & Jobs/Labor
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rapid rise of Generative AI (GenAI) tools has sparked debate over their role in complementing or replacing human workers across job contexts. We present a mathematical framework that models jobs, workers, and worker-job fit, introducing a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. We analyze how changes in subskill abilities affect job success, identifying conditions for sharp transitions in success probability. We also establish sufficient conditions under which combining workers with complementary subskills significantly outperforms relying on a single worker. This explains phenomena such as productivity compression, where GenAI assistance yields larger gains for lower-skilled workers. We demonstrate the framework' s practicality using data from O*NET and Big-Bench Lite, aligning real-world data with our model via subskill-division methods. Our results highlight when and how GenAI complements human skills, rather than replacing them.

Problem

• Modeling AI-human integration in jobs via skill decomposition
• Analyzing subskill impact on job success and transitions
• Determining conditions for complementary AI-human outperformance

Innovation

• Decompose skills into decision and action subskills
• Model job success with subskill ability changes
• Combine complementary subskills for better performance