# Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent

arXiv: https://arxiv.org/abs/2304.11279 · HTML: https://mlsanity.com/paper/2304.11279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 22 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods](https://mlsanity.com/paper/2205.00189/) (2022)
- [Finding Strategies Against Misinformation in Social Media: A Qualitative Study](https://mlsanity.com/paper/2205.00188/) (2022)
- [Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent](https://mlsanity.com/paper/2304.08759/) (2023)

## Built on by (most cited first, 2 of 2)

- [On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2510.12201/) (2025)
- [Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model](https://mlsanity.com/paper/2307.11765/) (2023)

## Abstract (arXiv)

The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no "true" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on platforms like Twitter, is a global problem for which there is currently no good solution. As an explainable AI (XAI) system, the agent provides explanations for its decisions on the misinformed nature of social media content. In this late-breaking study, we explored the roles of trust (attitude) and reliance (behaviour) as key elements of XAI user experience (UX) and whether these influenced the illusion of consensus. Findings show no effect of trust, but an effect of reliance on consensus-based explanations. This work may guide the design of anti-misinformation systems that use XAI, especially the user-centred design of explanations.