Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning

TL;DR

This paper investigates how AI impacts meaningful work, revealing mismatches between AI system traits and worker preferences, and advocates for AI design that enhances worker agency and happiness.

Contribution

It identifies gaps between AI system traits prioritized by developers and worker preferences, and proposes a research agenda to align AI design with meaningful work.

Findings

Tasks associated with agency and happiness are more exposed to AI.

Developers focus on politeness, strictness, and imagination; workers prefer straightforward, tolerant, and practical systems.

Call for AI design that explicitly supports meaningful work and worker needs.

Abstract

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.