Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning
This research addresses the problem of AI potentially reducing meaningful work for workers, highlighting a need for design changes to better align with worker needs.
The study investigated how workers perceive the meaningfulness of tasks exposed to AI and compared worker preferences with developer design traits, finding that tasks associated with agency or happiness are disproportionately exposed to AI and identifying design gaps such as workers preferring straightforward systems while developers emphasize politeness.
Prior work has mapped which workplace tasks are exposed to AI, but less is known about whether workers perceive these tasks as meaningful or as busywork. We examined: (1) which dimensions of meaningful work do workers associate with tasks exposed to AI; and (2) how do the traits of existing AI systems compare to the traits workers want. We surveyed workers and developers on a representative sample of 171 tasks and use language models (LMs) to scale ratings to 10,131 computer-assisted tasks across all U.S. occupations. Worryingly, we find that tasks that workers associate with a sense of agency or happiness may be disproportionately exposed to AI. We also document design gaps: developers report emphasizing politeness, strictness, and imagination in system design; by contrast, workers prefer systems that are straightforward, tolerant, and practical. To address these gaps, we call for AI whose design explicitly focuses on meaningful work and worker needs, proposing a five-part research agenda.