A Large-Scale Audit of Worker Preferences for AI Agent Automation and Augmentation

• Eeshwar Pasula

  University of Texas at Arlington

  Author

As AI agents are increasingly being integrated into work, little is known about the systematic knowledge among workers. We conduct a large audit of 1,500 workers across 104 US occupations, examining the preferred AI involvement in 844 occupational tasks. Our main contribution is the Human Agency Scale (HAS), which comprises 5 levels of human involvement between fully automated (H1) and essential human agency (H5). HAS seeks to circumvent the automation debate. The findings show that there is a 46.1% positive tendency to auto-automate tasks. According to research, this was largely to free up time for higher-value work (69.4%). Moreover, resistance has come from the creative sector as well. A significant mismatch is indicated by the comparison between what workers want and what 52 AI specialists think. We illustrate these mismatches as a desire-capability landscape consisting of four zones. The zones are Automation “Green Light”, Automation “Red Light”, R&D Opportunity, and Low Priority. Currently, investment is mostly concentrated in low-priority zones with unmet labour needs. H3 (Equal partnership) occupies 45.2% of the jobs, indicating an upcoming phase of collaboration. Interpersonal skills are becoming increasingly important. The implementation of AI technology and focused research should be guided by this worker-centred framework.

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