Enhancing User Acceptance of an AI Agent’s Recommendation in Information-Sharing Environments
Source abstract
Abstract
Information sharing (IS) occurs in almost every action daily. IS holds benefits for its users, but it is also a source of privacy violations and costs. Human users struggle to balance this trade-off. This reality calls for Artificial Intelligence (AI)-based agent assistance that surpasses humans’ bottom-line utility, as shown in previous research. However, convincing an individual to follow an AI agent’s recommendation is not trivial; therefore, this research’s goal is establishing trust in machines. Based on the Design of Experiments (DOE) approach, we developed a methodology that optimizes the user interface (UI) with a target function of maximizing the acceptance of the AI agent’s recommendation. To empirically demonstrate our methodology, we conducted an experiment with eight UI factors and n = 64 human participants, acting in a Facebook simulator environment, and accompanied by an AI agent assistant. We show how the methodology can be applied to enhance AI agent user acceptance on IS platforms by selecting the proper UI. Additionally, due to its versatility, this approach has the potential to optimize user acceptance in multiple domains as well.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in Service Interactions · Ethics and Social Impacts of AI · Technology Adoption and User Behaviour
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering, Department of Industrial Engineering and Management, Ariel University, Ariel 40700, Israel
Funding
Ariel Cyber Innovation Center in conjunction with the Israel National Cyber directorate in the Prime Minister’s Office
References (100+)
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