AI exposure predicts unemployment risk.

Autor/inn/en	Frank, Morgan; Ahn, Yong-Yeol; Moro, Esteban
Titel	AI exposure predicts unemployment risk. Gefälligkeitsübersetzung: KI-Exponierung prognostiziert Arbeitslosigkeitsrisiko.
Quelle	(2023), 35 S. PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2) Link als defekt meldenVerfügbarkeit
Reihe	arXiv papers
Sprache	englisch
Dokumenttyp	online; Monographie
Schlagwörter	Künstliche Intelligenz; Automatisierung; Technologische Entwicklung; Arbeitsplatzgefährdung; Arbeit; Arbeitslosigkeit; Arbeitsmarkt; Arbeitsmarktrisiko; Berufsgruppe; Qualifikationswandel; Arbeitspapier; Auswirkung; Entwicklung; Prognose; Prognosemodell; Regionaler Vergleich; Risiko; Zukunft; USA
Abstract	"Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure." The study refers to the period 2010-2020. (Author's abstract, IAB-Doku)..
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2024/1