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

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13725))

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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 Automated Occupation Classification based on Graph Convolutional Networks (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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Acknowledgement

This work is supported by the Australian Research Council (ARC) under Grant No. DP220103717, and LE220100078.

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Authors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu & Guandong Xu

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  1. Dawei Xu
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  2. Haoran Yang
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  3. Marian-Andrei Rizoiu
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  4. Guandong Xu
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Corresponding author

Correspondence to Guandong Xu .

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Editors and Affiliations

  1. The University of Adelaide, Adelaide, SA, Australia

    Weitong Chen

  2. The University of New South Wales, Sydney, NSW, Australia

    Lina Yao

  3. Macquarie University, Sydney, NSW, Australia

    Taotao Cai

  4. Griffith University, Brisbane, QLD, Australia

    Shirui Pan

  5. Microsoft, Beijing, China

    Tao Shen

  6. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

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Xu, D., Yang, H., Rizoiu, MA., Xu, G. (2022). Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-22064-7_37

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