A Study of Comfortability between Interactive AI and Human
Yi Ru Wang, Jiafei Duan, Sidharth Talia, Hao Zhu

TL;DR
This study explores user comfort levels with intent-predicting interactive AI systems, highlighting factors like control and privacy that influence comfort, based on empirical user studies.
Contribution
It introduces a novel study protocol and provides empirical insights into factors affecting user comfort with interactive AI systems.
Findings
Users feel comfortable if they have control over AI interactions.
Privacy concerns significantly impact user comfort.
Differentiation between AI and human responses does not greatly affect comfort.
Abstract
As the use of interactive AI systems becomes increasingly prevalent in our daily lives, it is crucial to understand how individuals feel when interacting with such systems. In this work, we investigate the comfort level of individuals when interacting with intent-predicting AI systems and identify the factors of influence. We introduce a study protocol to analyze human comfortability when interacting with intent-predicting AI systems and execute the study with over a dozen participants. The study findings suggest that users are comfortable with AI systems if they have control and their privacy is not affected. Additionally, the study found that users could differentiate between AI and human responses, but this did not significantly affect their comfort levels. This research paper's significance lies in its contribution to the growing body of literature on interactive AI systems, and it…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy, Security, and Data Protection · Behavioral Health and Interventions · Death Anxiety and Social Exclusion
