Algorithmic Decision-Making in Employment: Legal Challenges of Artificial Intelligence in Recruitment and Workplace Management

Abstract

Artificial intelligence is increasingly being used by organizations to automate recruitment, employee assessment, performance evaluation, workforce management, and promotion-related decisions. Algorithmic decision-making can improve organizational efficiency by processing large quantities of information and identifying patterns that may not be immediately visible to human decision-makers. However, the use of AI in employment environments also creates significant legal and technological concerns relating to discrimination, privacy, transparency, accountability, data protection, and human oversight. This article examines the technological foundations of AI-based employment decision-making and analyzes the principal legal challenges arising from its deployment in recruitment and workplace management. Particular attention is given to algorithmic bias, automated profiling, biometric monitoring, employee data, explainability, and responsibility for erroneous decisions. The article argues that efficiency and predictive accuracy alone cannot justify automated employment decisions where fundamental workplace rights may be affected. It proposes a human-centred governance framework based on transparency, data minimization, algorithmic auditing, explainability, human review, cybersecurity, and effective mechanisms for challenging automated decisions. The article concludes that AI can transform employment practices positively, but responsible deployment requires technological safeguards combined with appropriate legal and institutional accountability.