AI-Driven Insights: Personality Evaluation in Asynchronous Video Interviews for Informed Hiring Decisions

An organization spends a lot of time and money on interviews. Asynchronous interviews can help businesses cut costs and save time. In order to be considered for a position, an applicant must understand both the hard and soft skills needed for the position. These can be set up in the system, and filtration for earlier rounds can be performed based on AI assessment algorithms. In this paper, we present algorithms for evaluating candidates' soft talents. This research paper presents a comprehensive exploration of machine learning algorithms, including Support Vector Machines (SVM), Random Forest, Decision Trees, Logistic Regression, and XGBoost, as applied to personality assessment within the context of asynchronous video interviews. In the contemporary landscape of recruitment, asynchronous video interviews offer candidates a flexible platform to express themselves, transcending geographical constraints. We delve into the intricacies of the Big Five personality traits-openness, conscientiousness, extraversion, agreeableness, and neuroti-cism-and scrutinize the predictive capabilities of each algorithm in determining these traits from candidate responses. By incor-porating the optimal machine learning model into personality assessment, organizations can make more informed, data-driven hiring decisions, fostering the formation of high-performance teams while promoting equity and fairness in the recruitment process.