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2025
  • May 21 - 22, 2025
  • ADSM, Abu Dhabi
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International Conference on Artificial Intelligence Management and Trends

Conference Date: May 21, 2025

Abu Dhabi School of Management (ADSM), Abu Dhabi

Article

E-Recruitment in the UAE: An Artificial Intelligence Approach

Abdulla AldabalBusiness Analytics - Abu Dhabi School of Management - aldabal@jobx.ae
Published: Not specified https://doi.org/10.63962/RBAL8559
PDF downloadable

Abstract

This study proposes a hybrid AI framework for UAE e-recruitment, combining LLM, RAG, ML, and XAI to optimize candidate-job matching, support Emiratization, and ensure compliance with UAE Federal Law No. 45 on data protection. It automates recruitment while reducing bias and ethical risks through governance strategies. The research evaluates AI's impact on hiring productivity, equity, and efficiency using surveys, interviews, and ML analysis. It also provides guidelines for responsible AI adoption and a roadmap for developing recruitment systems aligned with UAE legal and societal expectations. The goal is fairer, more efficient hiring processes.

Keywords: E-Recruitment, Artificial Intelligence (AI), Emiratization, Machine Learning (ML), Large Language Models (LLMs), Natural Language Processing (NLP), Deep Learning (DL), Retrieval-Augmented Generation (RAG), Data Privacy, UAE Federal Law No. 45 of 2021, Ethical AI Governance, Algorithmic Fairness, Bias Reduction
I. INTRODUCTION
Recruitment processes are integral to organizational performance and the achievement of strategic objectives through human capital. Legacy online recruitment and onboarding systems struggle with mismatched candidate-job fit, biased screening, and lengthy hiring cycles—reducing efficiency and adversely affecting workforce diversity and quality. AI-driven solutions are poised to improve matching accuracy and reduce bias to enhance overall recruitment outcomes.

The UAE’s recruitment sector is expanding rapidly. In 2021, the Middle East and Africa online recruitment market was valued at USD 1.94 billion and is projected to reach USD 2.51 billion by 2028 (CAGR 3.8%). The landscape includes more than 155 competitors and 27 job boards (e.g., Bayt.com, LinkedIn, Naukrigulf.com, GulfTalent). Emiratization—part of the UAE National Innovation Strategy 2015—targets increased employment of UAE nationals to address private-sector dependence on expatriates (84.59% of employees). AI-powered e-recruitment systems can automate and improve matching, reduce inefficiencies, and support policy objectives.

This study develops an AI framework for candidate sourcing, bias reduction, and compliance with Federal Decree-Law No. 45 of 2021 on data protection, aiming to improve efficiency, support Emiratization, and ensure adherence to legal and ethical norms. :contentReference[oaicite:0]{index=0}
II. PROBLEM STATEMENT
The UAE job market faces systemic challenges that affect both employers and applicants. Employers encounter long time-to-hire, skills mismatches, and inefficient evaluation processes, while Emirati and expatriate candidates report discrimination and unsuitable offers. Traditional ATS workflows (rigid keyword filters, outdated rules) can exclude qualified applicants and perpetuate bias, raising fairness and inclusion concerns.

Although AI recruitment tools are increasingly used, gaps remain—particularly the lack of UAE-tailored frameworks that incorporate Emiratization, local data protection requirements, and explainability. The proposed hybrid AI framework (LLM + RAG + ML + XAI) addresses fairness, transparency, and compliance challenges specific to the UAE context. :contentReference[oaicite:1]{index=1}
III. LITERATURE REVIEW
AI and ML have transformed HR recruitment via predictive analytics, NLP, and deep learning, automating resume screening and improving candidate matching. In the UAE, recruitment encompasses job advertising, sifting, shortlisting, and interviewing—now heavily influenced by e-recruitment platforms and ATS such as Oracle Taleo, SAP SuccessFactors, and Workday.

Random Forest and ensemble methods support interpretability and robust classification, aiding transparency and bias mitigation. LLMs (e.g., ChatGPT, BERT) enhance job-candidate matching by processing structured and unstructured text, while RAG improves decision-making with real-time retrieval from external sources. Despite efficiency gains, the literature highlights persistent issues of bias, explainability, and compliance—reinforcing the need for UAE-specific, XAI-enabled frameworks aligned with Emiratization and data privacy law. :contentReference[oaicite:2]{index=2}
IV. METHODOLOGICAL APPROACH
The study employs mixed methods. Semi-structured interviews with HR managers, recruiters, AI specialists, and policy implementers explore attitudes toward AI, perceived benefits, and ethical/legal concerns. Surveys of HR professionals and applicants quantify satisfaction, fairness perceptions, and transparency (Likert scales and open-ended items).

Historical recruitment data (job postings, candidate profiles, outcomes) will train/validate ML models. A pilot deployment will compare AI recommendations with human decisions, measuring time-to-hire, matching accuracy, and user satisfaction, while maintaining strict ethical compliance (consent, confidentiality, anonymization) per institutional guidelines. :contentReference[oaicite:3]{index=3}
V. PROPOSED FRAMEWORK
The hybrid AI framework for UAE e-recruitment platforms (e.g., JobX.ae) integrates:
  • LLMs + NLP: for semantic parsing of CVs and job descriptions, multilingual support, and profile enrichment.
  • RAG: to retrieve labor-market trends, competency taxonomies, and up-to-date role requirements for informed matching.
  • ML (e.g., Random Forest, ensembles): for candidate scoring, anomaly detection, and robust classification.
  • XAI: for transparent explanations (feature-level rationales) to recruiters and candidates, supporting accountability and appeal.
The framework encodes Emiratization and compliance logic while balancing merit-based selection with policy goals.

System internal design for JobX (framework data flow)
Figure 2: System internal Design, JobX.

VI. CONCLUSION AND FUTURE OUTLOOK
Integrating LLMs, RAG, ML, and XAI can improve UAE e-recruitment by automating sourcing, screening, and matching; shifting recruiters toward strategic, human-centric tasks; and enhancing fairness, transparency, and compliance with Federal Decree-Law No. 45 of 2021. Encoding Emiratization within algorithmic logic supports national workforce goals without compromising merit.

Future work involves pilot implementations (e.g., JobX.ae) to evaluate usability, trust, fairness perceptions, time-to-hire, and long-term impacts on diversity, equity, performance, retention, and advancement, refining the framework iteratively with real-world feedback. :contentReference[oaicite:5]{index=5}

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