The Role of Artificial Intelligence and Neuroscience in Business Ethics: A Human Resources Perspective

Abstract

Artificial intelligence (AI) and neuroscience technologies are transforming the workplace at an accelerating pace, offering significant gains in operational efficiency, talent identification, and workforce analytics, while simultaneously generating profound ethical, legal, and social risks. This paper examines their impact on human resources (HR) management from a rigorous interdisciplinary perspective, integrating recent statistical data from Romania and European Union member states with insights from organisational psychology, applied ethics, and machine-learning theory. We analyse concrete applications in recruitment, performance evaluation, diversity management, and employee well-being, providing empirical data that illuminate prevailing trends and disparities. Key findings include: only 13.5% of EU enterprises had formally adopted AI by 2024, with Romania recording a mere 3.1% adoption rate (Eurostat, 2025), contrasted with a striking ground-level reality in which approximately 35% of Romanian office workers already use AI tools regularly (Romania Journal, 2024). This divergence between formal enterprise adoption and informal employee use is conceptualised as a complex governance problem-rather than a mere technological lag-situated at the intersection of organisational psychology, applied ethics, and machine-learning theory, a unified framing that underlies the entire analytical framework of this paper. We systematically identify ethical risks - algorithmic bias in automated hiring (exemplified by Amazon’s now-discontinued recruiting engine that penalised women’s resumes), covert neuro-surveillance via wearable EEG headsets, and opacity in AI-driven performance appraisal - and ground mitigation strategies in both the EU AI Act (2024) and the emerging Neurotechnology Framework. As a novel contribution, we propose and elaborate a mathematical optimisation model in which organisations maximise a utility function combining productivity gains, bias penalties, and privacy risk costs, subject to formal fairness (equal opportunity) and neurorights constraints. The model, solved via Lagrangian methods, is demonstrated through a detailed case study of a hypothetical Romanian technology firm. This structured, data-driven, ethically grounded approach offers concrete guidance for HR professionals, corporate governance bodies, and policymakers seeking to deploy AI and neurotechnology responsibly and in compliance with EU law.