AI-Powered Fuzzy Logic Model for Recruitment and Talent Management
Affiliation
| DOI | https://doi.org/10.67313/stanzaleafbookchapter.2026.31 Registered |
| Published In | Digital Narratives and the Future of English Studies Literature, Language, Media, and AI in the 21st Century |
| Book Editors | Dr. P. Nehru Dr. D G Kalaivani Dr Rini Melina P |
| ISBN | 978-81-180130-6-8 |
| Pages | 80-96 |
| Publication Date | 29 September 2026 |
| Publisher | Stanzaleaf Publication |
Abstract
Recruitment and talent management involve complex decisions in which many relevant criteria are qualitative, uncertain, incomplete, and difficult to measure precisely. Candidate experience may be described as “moderate,” technical competence as “high,” leadership potential as “promising,” and organizational readiness as “acceptable,” yet conventional scoring systems often force these evaluations into rigid numerical categories. Artificial intelligence can process large volumes of recruitment and employee data, but purely algorithmic approaches may introduce opacity, bias, and excessive dependence on historical data. Fuzzy logic provides a complementary decision framework because it represents imprecise human judgments through degrees of membership rather than binary classification. This chapter proposes an AI-powered fuzzy logic model for recruitment and talent management in which AI supports résumé parsing, skill extraction, competency mapping, pattern detection, and predictive analytics, while a fuzzy inference system translates uncertain human-resource criteria into interpretable decision recommendations. The proposed framework incorporates qualifications, professional experience, technical and behavioural skills, structured interview results, learning agility, performance indicators, and other job-relevant variables. These inputs are transformed into fuzzy linguistic categories such as low, medium, and high, processed through an explicit rule base, and converted into suitability or talent-potential scores through defuzzification. The model can support candidate ranking, person–job matching, development planning, succession management, internal mobility, and skill-gap identification. However, automated recommendations should not constitute final employment decisions. Fairness auditing, explainability, data protection, criterion validation, sensitivity analysis, and meaningful human review remain essential. The chapter argues that combining AI's computational capabilities with fuzzy logic's transparent treatment of uncertainty can provide a more interpretable decision-support architecture for contemporary human resource management.
Keywords
Artificial Intelligence, Fuzzy Logic, Recruitment, Talent Management, Personnel Selection, Person–Job Fit, Human Resource Analytics, Explainable AI, Decision Support, Fuzzy Inference System