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This podcast introduces WORKBank, a novel database designed to audit the integration of AI agents into various occupations by collecting dual perspectives from both domain workers and AI experts. It is based on the paper “Future of Work with AI Agents”  https://arxiv.org/pdf/2506.06576 . It establishes the Human Agency Scale (HAS) to quantify the desired level of human involvement, distinguishing between automation and augmentation. The findings reveal that workers generally prefer AI to automate low-value, repetitive tasks to free up time for high-value work, and they desire a balanced human-AI partnership (H3), often preferring more human agency than current AI capabilities allow. The research also highlights a mismatch between current AI investments and actual worker desires, suggesting a need to prioritize AI development in areas where workers express high desire for automation but current capabilities are low.

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