Role of artificial intelligence in analyzing human behavior and predicting personality traits and personality disorders.

Traditional approaches to the diagnosis of personality disorders, including a clinical interview and a self-report, are usually limited by subjectivity and time constraints. Recent developments in artificial intelligence have opened the possibility of more objective and data-driven psychological testing. This paper introduces an AI-powered system that forecasts personality disorders using natural language processing (NLP), speech recognition, and face recognition. The suggested method should help with the initial diagnosis and more tailored mental health solutions. Two benchmark datasets were used: myPersonality for text analysis and DAIC-WOZ for multimodal analysis of speech and facial expressions. The feature extraction methods were TF-IDF, Vader sentiment scores, Mel-Frequency Cepstral Coefficients, prosodic features, facial action units, and gaze tracking. BiLSTM, CNN, BERT, and GPT-3 models were analyzed through accuracy, precision, recall, F1 score, and AUC-ROC. GPT-3 was the most accurate at 89.1%, followed by BERT at 87.4% and CNN-based facial analysis at 85.6%. The findings indicate that multimodal fusion improves classification by leveraging holistic and complementary behavioral information. These results support the promise of multimodal systems with AI capabilities to make more precise predictions of personality disorders and underscore the need to consider interpretability, fairness, and data privacy in future applications. It should be mentioned that the current study deals with the problem of both normal personality traits prediction (through the Big Five framework) and features that can reflect psychological distress and be related to personality disorders. The myPersonality data set measures normative personality dimensions, whereas the DAIC-WOZ data set measures multimodal behavioral data that is relevant in clinical terms. The paper explains the connections between extreme trait profiles and clinical personality disorders to reconcile the paradigms of trait and disorder assessment.