Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

TL;DR

This paper explores how AI agents can facilitate inclusive feedback in meetings by acting as psychologically safe mediators, demonstrating improvements in meeting balance and quality, while also identifying organizational barriers to implementation.

Contribution

It introduces a sociotechnical system using AI for feedback exchange and empirically examines its effectiveness and organizational challenges.

Findings

AI feedback mediator improved speaking balance and meeting quality

Organizational barriers limited feedback exchange in real-world setting

AI can serve as psychologically safe feedback recipients

Abstract

Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study ($n=28$), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm ($n=10$) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.