Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents

TL;DR

This paper identifies key barriers to high-quality user feedback in conversational agents, analyzes their impact, and proposes design principles to improve feedback quality, emphasizing the need for advances in AI capabilities.

Contribution

It introduces four feedback barriers, derives design desiderata, and demonstrates that scaffolded systems can enhance user feedback quality in human-AI interactions.

Findings

Identified four feedback barriers: Common Ground, Verifiability, Communication, Informativeness.

Systems with scaffolds aligned to these barriers improve feedback quality.

Calls for AI advancements to address feedback barriers in conversational systems.

Abstract

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.