It is found 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, but retain ownership when they lead, iterate, or rewrite.
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.
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Artificial intelligence (AI) tools have become an integral part of organizational work, such as providing task‐relevant advice. Although existing research highlights the potential benefits of human‐AI collaboration for improving creative performance, recent findings question the universality of these effects. However...
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An exploratory analysis of nearly 6,000 real-world coding agent sessions is reported, finding that such records support analysis of turn authorship, degrees of human specification, and subsequent machine activity, but they cannot establish displaced choice or programmer awareness.
This workshop explores how design choices, such as reducing human-like AI design or communicating (un)certainty of responses, can support more informed and balanced use of AI.
Sarah Diefenbach, Daniel Ullrich, P. Preuschoff et al.· Adjunct Proceedings of the 1...· 0 citations
A five-year interview study with 17 Chinese digital painters, based on annual semi-structured interviews from 2021 to 2025, frames these accounts as longitudinal agency partitioning, the situated work of deciding which stages, responsibilities, values, and claims remain human in creative human-agent interaction.
Yi-Bo Meng, Rui-Qi Chen, Shu-Heng Cao et al.· 0 citations
A conceptual and formal framework for governed proactive agency is developed, organizing behavior across time through perception, intent, affective-conative appraisal, constraint, and feedback, and is intended to guide the development and evaluation of always-present personal assistants and embodied support systems tha...
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
MIT News · Artificial Intelligence· news.mit.eduJul 7, 2026
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
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