Journal of Psychiatry

Abstract

Towards a Deeper Understanding of Respondents to Personality and Clinical Self-Reports through Artificial Intelligence

Marcantonio Gagliardi* and Gian Luca Marcialis

While personality and clinical psychology have started using Artificial Intelligence (AI) to enhance their advancement, classical factor analysis methods remain the standard for self-report development. In our work, relying on the Attachment-Caregiving Questionnaire (ACQ), we suggest a different approach to self-report data analysis that might significantly benefit personality assessment, impacting clinical practice. We can understand respondents more deeply and outline their personality more precisely if we rely on a flexible interpretation of their answers based on contextual information about their history and present life. Despite expert scorers being able to perform this task, AI can be decisive in standardizing and automatizing the procedure, reaching both human accuracy and statistical consistency. Different implementation approaches can be adopted, and we plan to start testing as soon as enough completed ACQs are available. Big data could then be used to optimize item interpretation and improve performance.

Published Date: 2024-05-09; Received Date: 2024-04-08