Survey on Personality Detection on Multilingual Dataset using Machine Learning and Explainable AI

Abstract: Personality detection is a key area in AI, significantly impacting psychological profiling, behavioral analysis, and personalized recommendations. It addresses the new frontiers of multilingual personality detection using machine learning (ML) and Explainable AI (XAI). In this review paper, we studied the state-of-art research on deep learning and ensemble methods, while also highlighting the contributions of XAI frameworks like SHAP and LIME in improving model interpretation. We analyzed based metrics on use cases with fair and transparent measures to assess how ML methods generalize across languages and cultural settings through the survey of various multilingual dataset. Our findings highlight the significance of XAI in bridging AI predictions with human understanding, making AI-driven personality detection more interpretable and ethically responsible. This review contributes to personality computing by synthesizing advancements in ML-based personality detection, discussing challenges, and identifying future research directions to develop fair, accurate, and explainable AI-driven psychological assessments across languages.