Recruitment in the AI Era: Perspectives, Challenges, and Ethical Implications - A Systematic Literature Review

Abstract

Artificial intelligence (AI) is reshaping the recruitment process, from candidate attraction and résumé screening to assessment, profile matching, and decision support. Automated résumé screening, chatbots, automated video interviews, predictive analytics, gamification, and virtual reality contribute to faster, more consistent, and increasingly data-driven recruitment processes. However, these efficiency gains are accompanied by a significant paradox: the same systems may also reproduce historical biases, obscure decision criteria, intensify the collection and processing of personal data, and weaken the human dimension of the candidate experience.

Given a literature often focused on specific aspects of AI-assisted recruitment, this study proposes an integrative systematic review linking the main technologies, their operational performance, and their ethical implications. The search was carried out in Google Scholar, ScienceDirect, and Scopus and covered publications from 2015 to 2025. A total of 412 records were initially identified. After removing 124 duplicates, 288 publications were screened, including 50 full-text articles, resulting in a final corpus of 31 studies. The corpus was analyzed using explicit eligibility criteria, a structured extraction grid, and a qualitative thematic analysis.

The synthesis identifies six major families of AI applications and shows that the value of AI in recruitment does not depend solely on technical performance. Gains in speed, cost reduction, standardization, and decision support remain contingent on data quality, model transparency, privacy protection, regular auditing, and meaningful human oversight. The findings further indicate that fairness, trust, accountability, and candidate experience are closely linked to operational performance. The review concludes that responsible AI-assisted recruitment requires sociotechnical governance in which automated tools complement professional judgment while ensuring transparency, human oversight, and clearly defined accountability.

Classification JEL: M51, O33

Paper type: Theoretical Research (Systematic literature review)