XAIにおけるパーソナリティ駆動型パーソナライゼーション：ゲームプレイにおけるユーザー信頼の向上
Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay
ビッグファイブ性格特性から説明手法（決定木・テキスト・因子グラフ）の好みを予測する機械学習モデルを構築し、ナビゲーションゲームで推薦に適用したところ、ユーザーの信頼感が向上した。
著者: Zhaoxin Li, Sophie Yang, Shijie Wang
分類: cs.HC, cs.RO
原文アブストラクト
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.
