ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT: AN EXPANDED LITERATURE REVIEW WITH QUANTITATIVE PERSPECTIVES

ABSTRACT

The rapid integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is redefining how organizations acquire, develop, evaluate, and retain talent. This article synthesizes contemporary research on AI’s application to HR functions—recruitment, training and development, performance management, and compensation—while incorporating new conceptual and quantitative frameworks such as fuzzy logic–based decision models and explainable AI (XAI). The study emphasizes the dual nature of AI’s influence: operational efficiency, personalization, and strategic insights on one side, and ethical challenges, algorithmic opacity, and skill obsolescence on the other. A proposed fuzzy logic candidate scoring model, retention-cost optimization function, and performance deviation metric are presented to illustrate how AI can be embedded into decision support systems. The review concludes with strategic recommendations for ethically grounded, human-centric AI adoption.

АННОТАЦИЯ

Быстрая интеграция технологий искусственного интеллекта (ИИ) в управление человеческими ресурсами (УЧР) радикально меняет подход организаций к привлечению, развитию, оценке и удержанию талантов. В данной статье обобщаются современные исследования, посвящённые применению ИИ в функциях УЧР — подборе персонала, обучении и развитии, управлении результативностью и системе вознаграждений — с включением новых концептуальных и количественных моделей, таких как системы принятия решений на основе нечеткой логики и технологии объяснимого ИИ (XAI). Исследование подчёркивает двойственный характер влияния ИИ: с одной стороны — операционная эффективность, персонализация и стратегические инсайты, с другой — этические вызовы, непрозрачность алгоритмов и устаревание навыков. В работе предлагаются модель оценки кандидатов на основе нечеткой логики, функция оптимизации затрат на удержание сотрудников и метрика отклонения производительности, иллюстрирующие, как ИИ может быть встроен в системы поддержки принятия решений. Обзор завершается стратегическими рекомендациями по этически обоснованному, ориентированному на человека внедрению ИИ.

Keywords: artificial intelligence, human resource management, recruitment strategies, selection practices, training and development, performance management, compensation systems, fuzzy logic models.

Ключевые слова: искусственный интеллект, управление человеческими ресурсами, стратегии найма, практики отбора, обучение и развитие, управление результативностью, системы вознаграждений, модели на основе нечеткой логики.

1. Introduction

Artificial Intelligence has moved from experimental deployment to an operational necessity in HRM over the last decade. AI systems are now embedded in Applicant Tracking Systems (ATS), adaptive learning platforms, performance analytics dashboards, and dynamic compensation models [1]. These tools enable automation of repetitive administrative functions, freeing HR professionals to focus on strategy, workforce planning, and employee engagement. However, the expansion of AI into HR raises challenges. Algorithmic bias, data protection obligations, and the potential dehumanization of HR interactions have sparked debate [2]. The adoption of AI requires more than technological integration—it demands governance mechanisms that ensure fairness, transparency, and compliance. The convergence of AI capabilities with ethical oversight defines the future trajectory of HRM.

AI is broadly defined as the capacity of a system to analyze its environment and act autonomously toward defined objectives [3]. In HR contexts, AI extends beyond automation to predictive analytics, generative content creation, and natural language interpretation. Key AI technologies in HR include:

• Machine Learning (ML) for predictive hiring and retention modeling.
• Natural Language Processing (NLP) for resume parsing and sentiment analysis.
• Computer Vision for interpreting non-verbal interview cues.
• Generative AI for creating personalized training content and policy drafts [4].

The evolution of generative AI, including systems such as GPT-4, Bard, and DALL-E, has expanded HR’s ability to deliver hyper-personalized employee experiences and multilingual support..

2. Materials and Methods

This study follows a literature review methodology, synthesizing peer-reviewed research, case studies, and industry reports published between 2021–2025. Sources were selected from Scopus, Web of Science, and Google Scholar using keywords such as “artificial intelligence in HRM,” “fuzzy logic in recruitment,” and “explainable AI in HR.” The analysis combines qualitative review with the formulation of mathematical models for AI applications in HRM. The quantitative component focuses on:

• Fuzzy logic candidate scoring for recruitment decision-making.
• Training schedule optimization based on retention and cost functions.
• Performance deviation metrics for employee evaluation.
• Dynamic salary adjustment formulas based on market and performance data.

3. Results

Recruitment and Selection: AI-powered recruitment systems streamline candidate sourcing, screening, and engagement. These include ATS platforms, CRM systems for talent pipelines, and AI-enhanced video interviewing tools [5] (Table 1). A fuzzy logic candidate scoring model can formalize multi-criteria evaluation:

Where:

• S_c = candidate’s composite score
• w_i = weight of criterion i (e.g., technical skill, cultural fit)
• μ_i(x) = fuzzy membership value for criterion i

This method allows subjective attributes, such as adaptability, to be quantified. However, studies warn that excessive AI mediation in later recruitment stages can reduce candidate trust and perceived fairness [6].

Training and Development: AI customizes learning by adjusting content delivery to employees’ past performance, preferences, and career objectives. Deep learning–based “flight simulators” replicate job scenarios for safe skill practice. An optimal training schedule function can minimize retention loss and cost:     

Where:

• L(t) = knowledge decay over time t
• C(t) = training cost function
• α, β = organizational priorities

This supports evidence-based planning of training frequency and duration [7]. Performance Management: AI shifts performance management from periodic reviews to continuous, data-driven feedback loops. By applying predictive analytics, organizations can anticipate performance trends and act proactively [8]. A performance deviation metric can be defined as:

Where:

• D_p = mean deviation from target
• P_j^AI = AI-predicted performance for period j
• P_j^Target = target value

Low deviation values reflect strong alignment between employee outputs and organizational goals. Compensation and Benefits: AI supports dynamic compensation models by forecasting skill demand, salary trends, and benefit utilization [9].

An AI-based salary adjustment equation:

Where:

• M_skill = market skill index
• I_market = inflation index
• P_perf = performance rating
• γ1, γ2, γ3 = model coefficients

Table 1.

Summary, AI in HRM Functions

4. Discussion

The findings confirm that AI significantly enhances HRM efficiency, personalization, and strategic insight. The fuzzy logic model improves transparency in multi-criteria decisions, while optimization formulas help reduce training costs and improve scheduling accuracy.

However, challenges remain. Algorithmic bias can perpetuate inequalities if not mitigated. Transparency is hindered by black-box models, necessitating the adoption of Explainable AI (XAI) tools such as SHAP and LIME [10]. Moreover, data privacy and security compliance must be integral to AI adoption strategies.

Ethical AI adoption in HR must address:

• Bias Mitigation: Preventing reinforcement of discrimination through training data [10].
• Transparency: Using XAI tools like SHAP and LIME for decision interpretability.
• Data Governance: Ensuring GDPR compliance and secure handling of personal information.
• Human Oversight: Maintaining human judgment in sensitive HR decisions.

5. Conclusion

AI’s role in HRM is both transformative and complex. While it optimizes efficiency, personalization, and data-driven insight, it also requires robust governance to safeguard fairness and trust. Future research should examine:

• Hybrid AI-human recruitment models.
• Longitudinal effects of AI training personalization on employee retention.
• Scalability of fuzzy logic and multi-criteria decision models in enterprise HR systems.
• Cross-jurisdictional compliance strategies for AI-driven HR analytics.

References:

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3. Sheikh, H., Prins, C., & Schrijvers, E. (2023). Mission AI: Research for Policy.
4. Rahman, M., et al. (2025). Generative AI in Strategic HRM. International Journal of HR Technology.
5. Khawla, E. (2024). AI-Powered Talent CRM: Case Study. HRFlow.
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