  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. Interactive visualizations for this study are available
at https://sites.pitt.edu/~mrfrank/uiRiskDemo/.
