Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistance
Ali Aouad, Thodoris Lykouris, Huiying Zhong
TLDR
A new model explains how increased AI assistance can paradoxically degrade productivity and polarize skills due to unreliability or skill development.
Key contributions
- Proposes a human-AI interaction model to analyze AI's impact on productivity and skill.
- Identifies that AI unreliability or skill development endogeneity can cause a 'productivity paradox'.
- Demonstrates that skill polarization can emerge long-term due to varying AI literacy.
Why it matters
This paper is crucial for understanding AI's complex effects in the workplace. It provides a theoretical framework explaining why AI adoption doesn't always boost productivity and how it can reshape skill distribution. This offers insights for policy and design.
Original Abstract
Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setup, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating either endogeneity in skill development or in AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state when accounting for heterogeneity in AI literacy -- the agent's capability to identify and adapt to inaccurate AI outputs. Our results elucidate several mechanisms that may explain the emergence of human-AI productivity paradoxes and skill polarization, and identify simple measures that characterize when they arise.
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