Integrating Explainable AI (XAI) and NCA-Validated Clustering for an Interpretable Multi-layered Recruitment Model

This article proposes an interpretable, multi-layered recruitment model that balances predictive performance with decision transparency in AI-supported HR processes, ad-dressing risks related to opacity, auditability, and ethically sensitive decision-making. The architecture combines an expert rule layer for minimum-threshold screening, an unsupervised clustering layer to structure candidate profiles and generate pseudo-labels, and a supervised classification layer trained and evaluated via repeated k-fold cross-validation. Model behavior is explained using SHAP to identify feature contribu-tions to cluster assignment, and cluster quality is additionally diagnosed using Necessary Condition Analysis (NCA) to assess minimum competency requirements for attaining a target overall quality level. The approach is illustrated in a Data Scientist recruitment case study, where centroid-based clustering predominates (K-Means is most frequently se-lected), while linear classifiers show the highest effectiveness and stability (logistic re-gression performs best). SHAP highlights competencies that differentiate candidates beyond the initial threshold, and NCA further distinguishes candidates within the recommended cluster by identifying profiles that meet (or fail) the necessary-condition bottleneck. The proposed framework is replicable and supports transparent, auditable recruitment decisions.