This entry provides a comprehensive analysis of quota-based hiring practices in the age of artificial intelligence (AI), highlighting both the advantages and risks inherent in algorithmic decision-making for human resources. Starting with a definition of quota-based hiring—i.e., mandatory thresholds for underrepresented groups in terms of gender, ethnicity, disability, and other characteristics—the entry explores this phenomenon in the broader context of efforts to correct systemic bias in selection processes. Then, it maps the integration of AI into all the stages of recruitment. Starting from the most recent empirical and theoretical studies, a strengths, weaknesses, opportunities, and threats analysis of AI-enhanced hiring is proposed, detailing the strengths (shorter time-to-hire, expanded talent pool, detailed analysis of candidates, support for inclusion policies), weaknesses (bias in training datasets, recognition bias, opaque “black box” models, debiasing paradox), threats (implicit/explicit discriminations, regulatory vulnerabilities and critical issues, legal and reputational risks perpetuation of inequalities), and opportunities (increase Human Resources awareness, customized solutions for specific needs, more inclusive selection processes, continuous model monitoring). Part of the discussion is reserved to ethical considerations relating to transparency, fairness, and the “debiasing paradox.” This entry concludes by identifying some paths for future research, including the development of explainable AI techniques, standardized fairness metrics, and governance models that combine technical rigor with ethical management. Our goal is to propose a unified framework for AI-supported quota-hiring that maximizes inclusivity, complies with legal standards, and preserves human judgment in critical workforce decisions. Therefore, this theoretical investigation examines the state-of-the-art at the intersection of quota-based hiring and AI-mediated recruitment, with the goal of synthesizing contemporary empirical findings and legal-ethical arguments.