Beyond Explicit Instruction: Enhancing Human-AI Collaboration with Implicit User Feedback

Abstract

Successful human-AI teamwork depends on AI systems that can adjust to the evolving needs and situations of users. Rather than relying on explicit instructions from the user, an adaptable agent can make use of implicit feedback from end-users to infer user's behavioral and situational needs. Implicit information, such as user activity and eye tracking data, can help infer behavioral patterns that uncover the user's desires, requirements, and mental states. This method allows AI systems to deliver more tailored, proactive and wholistic assistance, which not only minimizes user’s real-time workload, but also serves to add redundancy to human-error, much like a beneficial human teammate. While this approach offers several potential advantages, there are practical difficulties in gathering and interpreting the data. Upcoming efforts to deduce high-level actions from low-level data will need to tackle these challenges to facilitate intuitive human-AI interactions and improve theefficacy of collaborative systems.

Keywords: human-ai teams, eye tracking, log analysis, large language models, implicit feedback

DOI: 10.54941/ahfe1006049