On the Automation of Job Tasks: Occupational exposure to Artificial Intelligence and Software

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On the Automation of Job Tasks: Occupational exposure to Artificial Intelligence and Software

While rapid advances in digital technologies transformed the occupational structures and workers‘ skill and task composition over the past decades, much less is known about how Artificial Intelligence technologies (AI) will shape future labour markets. As part of the “ai:conomics” project, we analyze the extent to which employees subject to social security contributions in Germany are potentially exposed to AI and software technology. Our results show that highly educated, high-income workers are most exposed to AI, while their exposure is lower to software. Overall, the findings suggest that given AI’s far-reaching potential to carry out different sets of tasks, these technologies are expected to impact workers across a wider skill and wage spectrum, which previous automation technologies had limited impact on.

These are the main findings:

  • To what extent can artificial intelligence (AI) and software systems (without AI) affect employees in different occupational groups in Germany? Using indicators that measure the automation potential of job tasks, we characterize occupations that could be substituted given the patented innovations in their field (Webb, 2020). We refer to these indicators as relative exposure potentials, as they enable comparisons between occupations.
  • We show that while the potential exposure to software is more likely to affect the job tasks of low or medium-educated employees, AI is more likely to have an impact on highly educated employees. This signals the distinct potential of AI in targeting different types of workers than other automation technologies.
  • The exposure potential to AI and software is particularly high for workers in the manufacturing industry and in information and communication technologies.
  • Occupations with a higher share of female employees appear to have lower AI and software exposure compared to those with a higher share of male employees.
  • Furthermore, we find that AI and software have the potential to take over slightly more tasks in occupations with skilled labour shortages, compared to occupations without shortages.
  • Nonetheless, our findings imply that even though the observed technologies can potentially automate certain tasks, not all tasks within an occupation can be carried out by AI.



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