AaJeeViKa: Trusted Explainable AI Based Recruitment Scheme in Smart Organizations

Abstract

The success of human resource management (HRM) closely synchronizes with the success of the prospective candidate (PCs) recruitment cycle (i.e. from job application to joining process of employee). However, finding the right PC according to the job description (JDs) is a complex task owing to manual background checks, and maintaining the auditability of the recruitment process by third-party recruitment (TPR) services. Recent studies have suggested the introduction of the blockchain (BC) and artificial intelligence (AI) in HRM processes to assure chronology, auditability, and automation, but limited approaches have discussed the use of explainable AI (xAI) for model interpretability. To address the issues, we propose a fusion scheme, AaJeeViKa, which integrates BC and explainable AI (xAI) to integrate trusted analytics in staffing and recruitment processes. The scheme generates a job suitability score (JSS), on which an interview call is sent to PC (cutoff threshold). The interview score and JSS score are added to form the employee reputation score (ERS), and the output prediction significance is computed by Shapley additive explanations (SHAP) explainers. The xAI result along with other information is meta-recorded and updated on BC ledgers. The results indicate that the scheme is highly beneficial for modern organizations to renovate their staffing and recruitment policies.