@misc{13730,
  abstract     = {{This paper introduces an LLM-mediated AI Advisor that contextualizes and synthesizes heterogeneous explainable AI (XAI) outputs to support fast and calibrated misinformation judgments in time-sensitive social media settings. We define LLM-mediated XAI as a process in which a large language model aggregates, prioritizes, and translates heterogeneous XAI outputs into a context-sensitive explanation tailored to the user’s decision situation. Semantic features, XAI modules and LLM-based summarization and synthesis enable the generation of explanations that are adapted in three ways: compressed for time-efficient decisions, translated into non-technical language, and progressively expandable for deeper inspection. Through a mixed-methods user study, including a quantitative study and a qualitative study, we analyze how users interpret, challenge and strategically rely on LLM-mediated explanations during real-world misinformation assessment tasks. The findings indicate that the approach reduces time-to-decision and supports critical inspection without inducing over-reliance. Progressive disclosure and different techniques to present information favored different user needs while conversational functionality was rarely used due to unclear benefits and fear of confusion.}},
  author       = {{Grimm, Valentin and Rubart, Jessica and Herder, Eelco and Röcker, Carsten}},
  booktitle    = {{WebSci Companion '26: Companion Publication of the 2026 18th ACM Web Science Conference}},
  editor       = {{Balke, Wolf-Tilo and Plötzky, Florian and Spaniol, Marc and Herder, Eelco and Manikonda, Lydia and Liu, Haiming and Ibáñez, Luis-Daniel and Rezapour, Rezvaneh}},
  isbn         = {{979-8-4007-2492-3}},
  keywords     = {{Large Language Model Mediation, Explainable AI, Decision Co- Pilot Systems, Misinformation Detection}},
  location     = {{Braunschweig}},
  pages        = {{110--116}},
  publisher    = {{ACM}},
  title        = {{{LLM-Mediated XAI Explanations: An AI Advisor for Fast and Calibrated Judgments on Potential Misinformation}}},
  doi          = {{https://doi.org/10.1145/3795513.3810452}},
  year         = {{2026}},
}