Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents
For researchers and designers of conversational AI, this work provides a framework to understand and address user feedback challenges, though the findings are based on two studies and may be incremental.
The paper identifies four feedback barriers (Common Ground, Verifiability, Communication, Informativeness) that prevent users from giving high-quality feedback to conversational agents, and shows that scaffolds aligned with design desiderata improve feedback quality.
High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and throughout model development. Yet despite its importance, human feedback to AI is often infrequent and low quality. This gap motivates a critical examination of human feedback during interactions with AIs. To understand and overcome the challenges preventing users from giving high-quality feedback, we conducted two studies examining feedback dynamics between humans and conversational agents (CAs). Our formative study, through the lens of Grice's maxims, identified four Feedback Barriers -- Common Ground, Verifiability, Communication, and Informativeness -- that prevent high-quality feedback by users. Building on these findings, we derive three design desiderata and show that systems incorporating scaffolds aligned with these desiderata enabled users to provide higher-quality feedback. Finally, we detail a call for action to the broader AI community for advances in Large Language Models capabilities to overcome Feedback Barriers.