# User Characteristics in Explainable AI: The Rabbit Hole of Personalization?

arXiv: https://arxiv.org/abs/2403.00137 · HTML: https://mlsanity.com/paper/2403.00137/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 12 of 12)

- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Persona Prompting as a Lens on LLM Social Reasoning](https://mlsanity.com/paper/2601.20757/) (2026)
- [People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI](https://mlsanity.com/paper/2403.08828/) (2024)
- [Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues](https://mlsanity.com/paper/2504.18483/) (2025)
- [Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Models](https://mlsanity.com/paper/2508.07658/) (2025)
- [PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations](https://mlsanity.com/paper/2603.06485/) (2026)
- [Dataset resulting from the user study on comprehensibility of explainable AI algorithms](https://mlsanity.com/paper/2411.02419/) (2024)
- [Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance](https://mlsanity.com/paper/2505.03674/) (2025)
- [Explanation User Interfaces: A Systematic Literature Review](https://mlsanity.com/paper/2505.20085/) (2025)
- [JEEVHITAA -- An HCAI Ecosystem to Support Collective Care](https://mlsanity.com/paper/2512.06364/) (2025)
- [Exploring Learners' Expectations and Engagement When Collaborating with Constructively Controversial Peer Agents](https://mlsanity.com/paper/2601.13479/) (2026)
- [XAI-Arena: Can LLMs Assess the Quality of XAI Explanations?](https://mlsanity.com/paper/2609.09428/) (2026)

## Abstract (arXiv)

As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable AI (XAI) has become critical for transparency and trust among users. A significant challenge in XAI is catering to diverse users, such as data scientists, domain experts, and end-users. Recent research has started to investigate how users' characteristics impact interactions with and user experience of explanations, with a view to personalizing XAI. However, are we heading down a rabbit hole by focusing on unimportant details? Our research aimed to investigate how user characteristics are related to using, understanding, and trusting an AI system that provides explanations. Our empirical study with 149 participants who interacted with an XAI system that flagged inappropriate comments showed that very few user characteristics mattered; only age and the personality trait openness influenced actual understanding. Our work provides evidence to reorient user-focused XAI research and question the pursuit of personalized XAI based on fine-grained user characteristics.