AI-OCI: A Novel Framework for Assessing AI’s Workforce Impact Using LLMs - Awuah-Gyasi, Frederick; Estrada, Trilce (Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2025)
We introduce the AI Occupational Capability Index (AI-OCI), a novel methodology for quantifying the alignment between AI model capabilities and the tasks that define human occupations. Unlike prior automation risk metrics, which rely on expert heuristics or job-level generalizations, AI-OCI operates at the task level by embedding and comparing over 19,000 occupational tasks with 338 AI capabilities using state-of-the-art language models. The resulting scores reveal how well AI systems can perform specific human functions, enabling interpretable, task-aligned assessments of labor exposure. Empirical evaluations show strong correlations with benchmark indices such as AIOE and GPT-4 Beta exposure scores, while diverging from legacy automation risk measures. We demonstrate AI-OCI’s utility through case-based analyses of employment and wage shifts across high-alignment occupations during the era of large language model adoption. The framework supports scalable, real-time tracking of AI’s workforce impact and provides a foundation for integrating labor intelligence into education, policy, and economic planning.