REVIEW 1 cited by
Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay
read the original 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.
Forward citations
Cited by 1 Pith paper
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.