Ownership in AI-Assisted Everyday Tasks
NeurIPS AI and the Self Workshop 2026
We report a 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.
- 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.
- Willingness to disclose AI use is often decoupled from actual pride or ownership, and instead shaped by community norms and fear of credit erasure.
Process drives ownership
When asked what made the difference in ownership between their two tasks, respondents highlighted that ownership was about who led the process. We also see ownership diminishes when the human simply approves the AI output. Some users are intentional about protecting their process to preserve ownership.
“ownership comes from owning the decisions made during the processing of the information. If there was no processing of information nor synthesis then it doesn't feel I own it.”
“I reviewed, I looked, I was in the loop, but I wasn't in charge.”
“I rewrite all communication generated by AI so I own it.”
Homogeneous voice, personalization, and rejection
For writing tasks, a prominent source of disownership was the voice. Respondents who invested in personalization of models reported retaining ownership. Interestingly, AI's bad outputs sometimes helped people build ownership by giving them a point of contrast.
“it wasn't my voice and I didn't like how ‘artificial’ it sounded”
“My model already has md file for content creation. This shows samples of my work, direct instructions about my tone, guardrails of what not to say or do, etc.”
“I don't always know what I want the final product to be until I see where the AI-generated version misses… even an imperfect output can be useful because the process of disagreeing with it or reshaping it becomes part of my own thinking.”
Enabling outputs beyond one's own abilities
Counterintuitively, respondents were less certain they could have done their high-ownership task without AI, where 42% answered “definitely yes” against 63% for low-ownership tasks. Some of the most-owned projects would have been impossible for their creators and unlocked new creative powers. When the human directs the project's vision, AI occupies the role of a hired illustrator or junior teammate, which does not threaten ownership. Meanwhile, disowned tasks were often things people could do, but chose to delegate: routine emails, lookups, and formatting.
“I'm proud and excited to see my vision in real life since it lived in my head for so many years. My inability to get it out of my head originally led me to stop writing children's books because of the illustration blocker.”
“As far as the deposition documents, it felt like directing a human paralegal. That's my job.”
“The difference was if it was a task that I wanted to have ownership of, or a task that I would have rather not have had to do in the first place.”
- High ownership · definitely not“wanted to build this sort of thing for over a decade, now I can code with my AI assistant(s)… It's all still mine, AI is just a translator.”
- High ownership · definitely yes“It is me - minus friction. My language and ideas.”
- High ownership · definitely yes“personalization and verification (done right) take time and keep my brain connected enough to make me feel like the final product is me.”
- Low ownership · definitely yes“it sounded nothing like me… AI emails sound very vanilla”
- Low ownership · definitely yes“I provided the intent and information… I feel more like I approved the output than authored it.”
- Low ownership · definitely not“In general, topics I process using AI AND I know little about, do not feel like my own work”
No ownership without comprehension
One route towards psychological ownership is intimately knowing the task. In our study, ownership failed when respondents could not understand the artifact or evaluate the output. Without comprehension, people felt little responsibility for maintaining and defending their work.
“Claude was as confused as me, so when the problem finally did get solved I felt like I actually played a role in getting to the solution and I fully understood what had happened”
“I really don't understand much of the codebase… I wouldn't consider this my work at all… honestly I'll probably be annoyed if I have to patch it in the future since I don't even feel responsible for it”
“I certainly don't know enough about the topic to judge and evaluate the output - hence does not feel like something I would call ‘mine’”
Disclosure
Respondents' willingness to disclose AI use varied widely and often depended on the situation. People cautiously open to disclosure generally preferred to narrate the process of AI usage rather than a label of AI-generated or not. Often, they fear credit erasure under a simple label. As a result of these complex social pressures, willingness to disclose AI use is not necessarily coupled with feelings of pride or ownership of the work.
“I would tell others how I used AI, but may feel uncomfortable if I had to state that AI was used for it without being able to give further explanation, as I would worry that people would assume the code itself was mostly or entirely AI generated, thus discounting my contribution.”
“Not that I'm not proud, but there is a stigma that I didn't do any work if I let people know I used AI… as if I don't think for myself or that I am lazy.”
“Proud, not really. Comfortable sharing, absolutely.”
What do people use AI for, and what did they choose to reflect on?
On the common tasks checklist, respondents reported a median of 9 of 17 task categories in the past 30 days. The most common were learning about a topic (94%) and looking up specific information (87%). Of the tasks respondents chose to reflect on, only a handful were lookup- or learning-related tasks. Tasks respondents chose to reflect on were often writing and technical tasks. Ownership reflections often involved tasks that produced a significant artifact rather than informational use from day-to-day interaction.
Grey: share of checklist respondents (n = 53) who used AI for each category in the past 30 days. Blue: share of the 107 reflected tasks mapped to that category.
Recommendations
Our findings motivate future research in the following themes:
Focus on process
How we can increase process visibility for users and collaborators?
Does recording and displaying the interaction history strengthen felt ownership and calibrate self-attribution? How does visibility of a collaborator's process reshape ownership in shared projects? Can we measure adverse effects when people don't take ownership of the process (e.g., increased effort for reviewers)?
Personalization
What are the effects of perceived vs actual personalization? Several respondents felt they had uniquely scaffolded their AI to adapt to them, making it now their AI.
Are these personalization methods actually unique and effective? How does informing people that their scaffolding methods are actually similar change their perception of ownership?
Enabling the previously impossible
People can feel great ownership over outputs that they could not have produced on their own, like illustrating a graphic novel. However, it is not clear how this sense of ownership coexists with low expertise in some areas of the output.
Is this effect primarily present in cases where users may be strong evaluators but not strong producers of the work (e.g., the art critic vs. the artist)?
Promoting honest disclosure of AI use
People can more authentically own their work and learn healthy modes of human-AI collaboration when communities encourage honesty around disclosing AI use. What factors encourage or discourage the producer from honestly disclosing AI use when it is required?
What formats of disclosure are most effective (e.g., categorical vs. narrative)? How does social feedback on output quality affect willingness to disclose use?
Cite our work
@inproceedings{wei2026ownership,
title = {Ownership in AI-Assisted Everyday Tasks},
author = {Wei, Megan and Subbiah, Melanie and Lee, Audrey and Dahmani, Annya and Edwards, Dave and Edwards, Helen and Pavlick, Ellie},
booktitle = {NeurIPS AI and the Self Workshop},
year = {2026},
eprint = {2609.20658},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20658}
}