When Assurance Undermines Intelligence: The Efficiency Costs of Data Governance in AI-Enabled Labor Markets

TL;DR

This study shows that strict data governance for AI in labor markets can reduce AI efficiency, leading to worse job matching and higher turnover, especially affecting small, fast-growing firms.

Contribution

It provides empirical evidence on how data assurance policies can unintentionally hinder AI performance in real-world digital labor platforms.

Findings

Restricting data use decreased AI-driven job matching efficiency.

Higher employee turnover observed after data restrictions.

Small and fast-growing firms were most affected.

Abstract

Generative artificial intelligence (GenAI) like Large Language Model (LLM) is increasingly integrated into digital platforms to enhance information access, deliver personalized experiences, and improve matching efficiency. However, these algorithmic advancements rely heavily on large-scale user data, creating a fundamental tension between information assurance-the protection, integrity, and responsible use of privacy data-and artificial intelligence-the learning capacity and predictive accuracy of models. We examine this assurance-intelligence trade-off in the context of LinkedIn, leveraging a regulatory intervention that suspended the use of user data for model training in Hong Kong. Using large-scale employment and job posting data from Revelio Labs and a Difference-in-Differences design, we show that restricting data use significantly reduced GenAI efficiency, leading to lower matching rates, higher employee turnover, and heightened labor market frictions. These effects were especially pronounced for small and fast-growing firms that rely heavily on AI for talent acquisition. Our findings reveal the unintended efficiency costs of well-intentioned data governance and highlight that information assurance, while essential for trust, can undermine intelligence-driven efficiency when misaligned with AI system design. This study contributes to emerging research on AI governance and digital platform by theorizing data assurance as an institutional complement-and potential constraint-to GenAI efficacy in data-intensive environments.