Akademik Veri Yönetim Sistemi
Journal of Chinese Economic and Business Studies, 2026 (ESCI, Scopus)
This paper examines how AI-driven hiring can reconcile efficiency with fairness and social welfare by embedding ethical and ESG principles directly into economic optimisation. We develop an equilibrium model in which firms minimise recruitment costs and maximise match precision through a multi-stage algorithmic screening process. In the baseline, efficiency gains arise from shifting screening and adjustment costs onto applicants, which reduces participation, increases inequality, and lowers welfare. We introduce an ethical extension that incorporates three constraints. These are a firm cost-sharing floor, an applicant time cap, and a demographic parity condition. These preserve equilibrium uniqueness and tractability while aligning private optimisation with social objectives. The results show that ethical design transforms AI hiring from a narrow efficiency mechanism into a system of shared and sustainable value creation. The model provides a quantitative foundation for ESG-aligned governance and practical guidance for designing AI systems that are efficient, inclusive, and accountable.