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 betweenAI model capabilities and the tasks that define humanoccupations. Unlike prior automation risk metrics, whichrely on expert heuristics or job-level generalizations,AI-OCI operates at the task level by embedding andcomparing over 19,000 occupational tasks with 338 AIcapabilities using state-of-the-art language models. Theresulting scores reveal how well AI systems can performspecific human functions, enabling interpretable,task-aligned assessments of labor exposure. Empiricalevaluations show strong correlations with benchmark indicessuch as AIOE and GPT-4 Beta exposure scores, whilediverging from legacy automation risk measures. Wedemonstrate AI-OCI’s utility through case-based analyses ofemployment and wage shifts across high-alignmentoccupations during the era of large language modeladoption. The framework supports scalable, real-timetracking of AI’s workforce impact and provides a foundationfor integrating labor intelligence into education, policy,and economic planning.