# VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication

arXiv: https://arxiv.org/abs/2503.07279 · HTML: https://mlsanity.com/paper/2503.07279/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- ["According to ...": Prompting Language Models Improves Quoting from Pre-Training Data](https://mlsanity.com/paper/2305.13252/) (2023)
- [Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving](https://mlsanity.com/paper/2405.01379/) (2024)
- [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023)
- [Language Models in Dialogue: Conversational Maxims for Human-AI Interactions](https://mlsanity.com/paper/2403.15115/) (2024)
- [Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questions](https://mlsanity.com/paper/1905.10700/) (2019)
- [Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants](https://mlsanity.com/paper/2108.01923/) (2021)
- [Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems](https://mlsanity.com/paper/2203.12981/) (2022)
- [Human-AI Interactions Through A Gricean Lens](https://mlsanity.com/paper/2106.09140/) (2021)
- [Trust in AI and Implications for the AEC Research: A Literature Analysis](https://mlsanity.com/paper/2203.03847/) (2022)
- [Human-centered trust framework: An HCI perspective](https://mlsanity.com/paper/2305.03306/) (2023)
- [What Drives Virtual Influencer's Impact?](https://mlsanity.com/paper/2301.09874/) (2023)

## Abstract (arXiv)

Trust plays a fundamental role in shaping the willingness of users to engage and collaborate with artificial intelligence (AI) systems. Yet, measuring user trust remains challenging due to its complex and dynamic nature. While traditional survey methods provide trust levels for long conversations, they fail to capture its dynamic evolution during ongoing interactions. Here, we present VizTrust, which addresses this challenge by introducing a real-time visual analytics tool that leverages a multi-agent collaboration system to capture and analyze user trust dynamics in human-agent communication. Built on established human-computer trust scales-competence, integrity, benevolence, and predictability-, VizTrust enables stakeholders to observe trust formation as it happens, identify patterns in trust development, and pinpoint specific interaction elements that influence trust. Our tool offers actionable insights into human-agent trust formation and evolution in real time through a dashboard, supporting the design of adaptive conversational agents that responds effectively to user trust signals.