Ownership in AI-Assisted Everyday Tasks

Problem

This preprint addresses a gap in the understanding of how AI impacts the sense of ownership in collaborative tasks. The authors highlight the need for insights into the dynamics of human-AI collaboration and its effects on individual ownership perceptions.

Method

The study employs an exploratory qualitative survey methodology. Participants were self-selected individuals who provided descriptions of two tasks they completed with AI assistance: one task where they felt a sense of ownership and another where they did not. This approach allowed the authors to gather nuanced insights into the participants' experiences and perceptions regarding ownership in the context of AI collaboration.

Results

The available text does not report quantitative results. However, several key findings emerged from the qualitative data:

• Ownership is significantly influenced by the collaboration process involved in task completion.
• Participants reported a phenomenon of disowning when they merely approved AI suggestions without active engagement.
• High levels of ownership were associated with tasks that could not have been completed without AI assistance, indicating a strong link between reliance on AI and ownership perception.
• A loss of personal voice in the task process was noted to erode feelings of ownership among participants.
• The willingness to disclose the use of AI in tasks was found to be influenced by prevailing community norms, suggesting social factors play a role in ownership perceptions.

Limitations

The authors acknowledge that the qualitative nature of the study limits the generalizability of the findings. Additionally, self-selection bias may affect the representativeness of the participant responses, as those who chose to participate may have different perspectives compared to a randomly selected sample.

Why it matters

Understanding the relationship between AI assistance and ownership in collaborative tasks has significant implications for the design of AI systems and their integration into everyday workflows. Insights from this research could inform the development of AI tools that enhance user engagement and ownership, ultimately leading to more effective human-AI collaboration. Furthermore, recognizing the social dynamics at play can guide future research and applications in AI ethics and user experience design.