JAM discovers theory-invariant pseudo-facets via attention-pooled graph prototypical networks, Cross-Theory Harmonization, and LLM-as-a-Judge, improving cross-framework balanced accuracy on Essays and Kaggle datasets.
Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay
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abstract
Tailoring XAI methods to individual needs is crucial for intuitive Human-AI interactions. While context and task goals are vital, factors like user personality traits could also influence method selection. Our study investigates using personality traits to predict user preferences among decision trees, texts, and factor graphs. We trained a Machine Learning model on responses to the Big Five personality test to predict preferences. Deploying these predicted preferences in a navigation game (n=6), we found users more receptive to personalized XAI recommendations, enhancing trust in the system. This underscores the significance of customization in XAI interfaces, impacting user engagement and confidence.
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cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition
JAM discovers theory-invariant pseudo-facets via attention-pooled graph prototypical networks, Cross-Theory Harmonization, and LLM-as-a-Judge, improving cross-framework balanced accuracy on Essays and Kaggle datasets.