Application of Artificial Intelligence and Fuzzy Logic for Assessing Legal Risks of Personnel Decisions in the Human Resource Management System
Abstract
Purpose. To develop a hybrid model combining artificial intelligence and fuzzy logic for assessing the legal risks of personnel decisions within the legal support system of human resource management.
Methods. The study employed systems analysis, logical-predicate modelling, a risk-oriented approach, fuzzy logic, Mamdani rules, centroid defuzzification, natural language processing technologies, Retrieval-Augmented Generation, machine learning, expert systems, knowledge graphs, and the Human-in-the-Loop concept.
Findings. An architecture of an intelligent system for assessing the legal risks of personnel decisions is proposed. It integrates modules for personnel data processing, retrieval of legal grounds, LLM/RAG-based analysis, an expert rule base, a knowledge graph, a Mamdani fuzzy inference system, machine learning, explainability tools, and human oversight. The input variables include document completeness, procedural compliance, confirmation of authority, protection of individual rights, and personal data protection. A conditional example demonstrates a decrease in the integral legal risk from (R_{legal}=0.50) to (R_{legal}=0.396) after improvements in documentary support and procedural compliance.
Practical implications. The model can be used for the preliminary assessment of personnel decisions and for generating explainable recommendations under mandatory human oversight.
Limitations/Future research. The model is conceptual and methodological in nature. The membership functions require validation using real personnel cases, while the demonstrative Mamdani rule base needs to be expanded for different types of personnel decisions. Future research should focus on developing a software prototype, creating a dataset of personnel-related legal cases, and testing the accuracy of AI modules.
Paper type: Methodological.
Downloads
References
Elsevier. (n.d.). Scopus content policy and selection. Retrieved July 8, 2026, from https://www.elsevier.com/products/scopus/content/content-policy-and-selection.
Organisation for Economic Co-operation and Development. (2024). AI principles. https://www.oecd.org/en/topics/ai-principles.html.
Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1.
European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No. 300/2008, (EU) No. 167/2013, (EU) No. 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj.
European Union. (2024). Artificial Intelligence Act: Annex III—High-risk AI systems. https://artificialintelligenceact.eu/annex/3/
Shaout, A., & Al-Shammari, M. (1998). Fuzzy logic modeling for performance appraisal systems: A framework for empirical evaluation. Expert Systems with Applications, 14(3), 323–328. https://doi.org/10.1016/S0957-4174(97)00085-7.
Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066.
Dursun, M., & Karsak, E. E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37(6), 4324–4330. https://doi.org/10.1016/j.eswa.2009.11.067.
Kusumawardani, R. P., & Agintiara, M. (2015). Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Procedia Computer Science, 72, 638–646. https://doi.org/10.1016/j.procs.2015.12.173.
Hoque, S., Karim, A. A. J., Alam, M. G. R., & Gope, N. (2026). When LLM meets Fuzzy-TOPSIS for personnel selection through automated profile analysis. IEEE Access, 14, 15778–15794. https://doi.org/10.1109/ACCESS.2026.3658575.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://ssrn.com/abstract=2477899.
Organisation for Economic Co-operation and Development. (2024). AI principles. https://www.oecd.org/en/topics/ai-principles.html.
Binns, R. (2019). Human judgement in algorithmic loops: Individual justice and automated decision-making. SSRN. https://ssrn.com/abstract=3452030.
North Atlantic Treaty Organization. (2024, July 10). Summary of NATO’s revised artificial intelligence (AI) strategy. https://www.nato.int/en/about-us/official-texts-and-resources/official-texts/2024/07/10/summary-of-natos-revised-artificial-intelligence-ai-strategy.
North Atlantic Treaty Organization. (2021, October 22). Summary of the NATO artificial intelligence strategy. https://www.nato.int/en/about-us/official-texts-and-resources/official-texts/2021/10/22/summary-of-the-nato-artificial-intelligence-strategy [Belgium].
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2.
Abstract views: 9 PDF Downloads: 7
Copyright (c) 2026 Юрий Гусак, Денис Кандуєв
This work is licensed under a Creative Commons Attribution 4.0 International License.
