aiqbench analysis
This study investigates the psychological boundaries of authorship when AI collaborates on everyday tasks, finding that users retain a sense of ownership only when they actively lead, iterate, or rewrite the output rather than passively approving suggestions. The research highlights how loss of personal voice and lack of comprehension erode this feeling, while disclosure behaviors are often driven by social norms rather than genuine pride in the work. These findings offer critical insights for developing AI systems that preserve human agency and authorship in an increasingly automated workflow.
Ownership in AI-Assisted Everyday Tasks
arXiv:2609.20658v1 Announce Type: new Abstract: When does work done with AI still feel like ours? As AI becomes woven into everyday tasks, we must examine what happens to our sense of ownership and contribution when a machine shares in producing what we make. We report an exploratory qualitative survey in which participants were asked to describe two recent, self-selected tasks completed with AI: one that felt like their own and one that did not. We find that felt ownership depends on the process of collaboration: people disown work when they merely approve AI's suggestions, but retain ownership when they lead, iterate, or rewrite. Ownership can also extend to settings where people own the vision for a project but not the execution; respondents reported high ownership on tasks they could not have completed without AI. Loss of personal voice and a lack of comprehension of the output both erode ownership. Finally, willingness to disclose AI use is often decoupled from actual pride or ownership, and instead shaped by community norms and fear of credit erasure. We propose several research directions as a result of these findings to promote AI development that supports people's sense of authorship over their own lives.
Abstract:When does work done with AI still feel like ours? As AI becomes woven into everyday tasks, we must examine what happens to our sense of ownership and contribution when a machine shares in producing what we make. We report an exploratory qualitative survey in which participants were asked to describe two recent, self-selected tasks completed with AI: one that felt like their own and one that did not. We find that felt ownership depends on the process of collaboration: people disown work when they merely approve AI's suggestions, but retain ownership when they lead, iterate, or rewrite. Ownership can also extend to settings where people own the vision for a project but not the execution; respondents reported high ownership on tasks they could not have completed without AI. Loss of personal voice and a lack of comprehension of the output both erode ownership. Finally, willingness to disclose AI use is often decoupled from actual pride or ownership, and instead shaped by community norms and fear of credit erasure. We propose several research directions as a result of these findings to promote AI development that supports people's sense of authorship over their own lives.
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2609.20658 [cs.AI]
(or arXiv:2609.20658v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.20658 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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