Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks

• URL: http://arxiv.org/abs/2209.02182v1
• Date: Tue, 6 Sep 2022 02:19:50 GMT
• Title: Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks
• Authors: Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu, and Guandong Xu
• Abstract summary: Rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations. Recent social-economic studies suggest that nearly 50% of occupations are at high risk of being automated in the next decade. We propose a graph-based semi-automated classification method named textbfAutomated textbfOccupation textbfClassification to identify the automated risk for occupations.
• Score: 13.092145058320316
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50\% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations' task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named \textbf{A}utomated \textbf{O}ccupation \textbf{C}lassification based on \textbf{G}raph \textbf{C}onvolutional \textbf{N}etworks (\textbf{AOC-GCN}) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations' local and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals' decision-making before entering the job market.

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