{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:46:30Z","timestamp":1760575590703,"version":"build-2065373602"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIES"],"abstract":"<jats:p>We introduce the AI Occupational Capability Index (AI-OCI),\na novel methodology for quantifying the alignment between\nAI model capabilities and the tasks that define human\noccupations. Unlike prior automation risk metrics, which\nrely on expert heuristics or job-level generalizations,\nAI-OCI operates at the task level by embedding and\ncomparing over 19,000 occupational tasks with 338 AI\ncapabilities using state-of-the-art language models. The\nresulting scores reveal how well AI systems can perform\nspecific human functions, enabling interpretable,\ntask-aligned assessments of labor exposure. Empirical\nevaluations show strong correlations with benchmark indices\nsuch as AIOE and GPT-4 Beta exposure scores, while\ndiverging from legacy automation risk measures. We\ndemonstrate AI-OCI\u2019s utility through case-based analyses of\nemployment and wage shifts across high-alignment\noccupations during the era of large language model\nadoption. The framework supports scalable, real-time\ntracking of AI\u2019s workforce impact and provides a foundation\nfor integrating labor intelligence into education, policy,\nand economic planning.<\/jats:p>","DOI":"10.1609\/aies.v8i1.36545","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T13:18:00Z","timestamp":1760534280000},"page":"238-248","source":"Crossref","is-referenced-by-count":0,"title":["AI-OCI: A Novel Framework for Assessing AI\u2019s Workforce Impact Using LLMs"],"prefix":"10.1609","volume":"8","author":[{"given":"Frederick","family":"Awuah-Gyasi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trilce","family":"Estrada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,10,15]]},"container-title":["Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/download\/36545\/38683","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/download\/36545\/38683","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T13:18:00Z","timestamp":1760534280000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/view\/36545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,10,15]]}},"URL":"https:\/\/doi.org\/10.1609\/aies.v8i1.36545","relation":{},"ISSN":["3065-8365"],"issn-type":[{"value":"3065-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}