| dc.description.abstract | E-recruitment has revolutionized the hiring process, enabling organizations to efficiently
identify and evaluate candidates through digital platforms. Recently, personality
prediction systems have emerged as a crucial tool in this domain. Powered by AI and
machine learning, these systems analyze candidates’ CVs and personal statements to
infer personality traits such as extroversion, conscientiousness, and creativity, aligning
job roles with personality profiles. This review examines current research on the
methodologies employed in AI-based personality prediction systems, their predictive
accuracy, and the ethical considerations surrounding their use. Findings highlight
that while these systems offer benefits like enhanced candidate screening and decision making efficiency, they face challenges, including moderate accuracy due to data
variability, algorithmic bias, and privacy concerns in data processing. The review
underscores the need for ethical guidelines and diverse datasets to improve these
systems and advocates for balancing human judgment with AI-driven assessments.
Future recommendations include enhancing model accuracy, addressing biases, and
fostering transparent, non-discriminatory e-recruitment practices. This study bridges
knowledge gaps by exploring the potential and limitations of personality prediction
systems in modern recruitment, contributing to the development of fair and effective
AI-driven hiring processes. | en_US |
