Inteligencia artificial en procesos de selección de personal: sesgos y oportunidades
La adopción de tecnologías de inteligencia artificial (IA) en los procesos de reclutamiento y selección de personal ha experimentado un crecimiento exponencial, prometiendo mayor eficiencia y reducción de sesgos humanos. Sin embargo, esta transformación tecnológica también ha generado preocupaciones significativas sobre la equidad, transparencia y el potencial de perpetuar discriminaciones existentes. Este artículo analiza los principales tipos de sesgos identificados en sistemas de IA para selección, las oportunidades y beneficios de su implementación, así como los desafíos éticos y regulatorios que enfrentan las organizaciones. Mediante una revisión sistemática de la literatura reciente (2018-2025), se identifican mejores prácticas y recomendaciones para una implementación responsable de la IA en recursos humanos. La implementación de inteligencia artificial en la selección de personal puede mejorar la eficiencia organizacional, pero requiere regulación, supervisión ética y mecanismos de equidad para evitar sesgos y garantizar procesos justos y centrados en el bienestar humano.
Agbasiere, C., & Nze-Igwe, G. (2025). Algorithmic Fairness in Recruitment: Designing AI-Powered Hiring Tools to Identify and Reduce Biases in Candidate Selection. Path of Science. https://doi.org/10.22178/pos.116-10
Ajunwa, I. (2020). The paradox of automation as anti-bias intervention. Cardozo Law Review, 41, 1671-1742. https://dx.doi.org/10.2139/ssrn.2746078
Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring. AI. https://doi.org/10.3390/ai5010019
Allal-Chérif, O., Aránega, A., & Sánchez, R. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822. https://doi.org/10.1016/j.techfore.2021.120822
Aminou, L., Daaif, A., Soulami, M., Chalfaouat, A., & Youssfi, M. (2024). Converging human and algorithmic biases in the hiring decision-making process. 2024 International Conference on Intelligent Systems and Computer Vision (ISCV), 10620077. https://doi.org/10.1109/iscv60512.2024.10620077
Basha, H. S. A., Rajitha, N., Roslin, J. A., Thoti, K. K., Khalid, M. R. P., & Mishra, B. R. (2024). AI-powered recruitment and employee selection: Evaluating bias and fairness in hiring practices. European Economic Letters, 14(1), 1107. https://doi.org/10.52783/eel.v14i1.1107
Biradar, A., Ainapur, J., Kalyanrao, K., Aishwarya, A., Sudharani, S., Shivaleela, S., & Monika, M. (2024). The impact of artificial intelligence on modern recruitment practices: A multi-company case study analysis. IOSR Journal of Business and Management, 13(9), 143150. https://doi.org/10.35629/8028-1309143150
Castro Alfaro, A. (2024). Impacto de la IA en la educación y la investigación. Enfoque Disciplinario, 9(2), 44-52. https://doi.org/10.70165/enfdis.v9i2.296
Chang, X. (2023). Gender bias in hiring: An analysis of the impact of Amazon’s recruiting algorithm. Advances in Economics, Management and Political Sciences, 23, 367. https://doi.org/10.54254/2754-1169/23/20230367
Chen, Z. (2022). Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cognition, Technology & Work (Online), 25, 135 - 149. https://doi.org/10.1007/s10111-022-00716-0
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10, 1-12. https://doi.org/10.1057/s41599-023-02079-x
Coy García, G., Fuel Bermeo, A., Durán Pardo, V., & Coloma Añazco, J. (2024). La inteligencia artificial aplicada a la enseñanza de la matemática. Conocimiento Global, 9(1), 234-242. https://doi.org/10.70165/cglobal.v9i1.357
Da Silva, A., & Da Costa, E. (2025). Fair by Design? The Legal and Ethical Challenges of Algorithmic Hiring. Studies in Law and Justice. https://doi.org/10.56397/slj.2025.08.01
Dadheech, R., Karichalil, R., , K., & Sindhu, N. (2025). AI in Recruitment Enhancing Efficiency or Replacing Human Judgement. Journal of Informatics Education and Research. https://doi.org/10.52783/jier.v5i2.2817
Drage, E., & Mackereth, K. (2022). Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”. Philosophy & Technology, 35. https://doi.org/10.1007/s13347-022-00543-1
Fabris, A., Baranowska, N., Dennis, M., Graus, D., Hacker, P., Saldivar, J., Zuiderveen Borgesius, F., & Biega, A. J. (2024). Fairness and bias in algorithmic hiring: A multidisciplinary survey. ACM Transactions on Intelligent Systems and Technology, 15(6), 1-57. https://doi.org/10.1145/3696457
Fabris, A., Baranowska, N., Dennis, M., Graus, D., Hacker, P., Saldivar, J., Borgesius, F., & Biega, A. (2023). Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey. ACM Transactions on Intelligent Systems and Technology, 16, 1 - 54. https://doi.org/10.1145/3696457
Faroozan, A. (2025). The evolving role of artificial intelligence in recruitment: Efficiency, bias mitigation, and ethical challenges. International Journal For Multidisciplinary Research, 7(1), 34682. https://doi.org/10.36948/ijfmr.2025.v07i01.34682
Gunjawale, P. (2025). Adaptive Job Search and Recruitment Using AI. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. https://doi.org/10.55041/ijsrem42663
Hunkenschroer, A., & Luetge, C. (2022). Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda. Journal of Business Ethics, 178, 977 - 1007. https://doi.org/10.1007/s10551-022-05049-6
Hurtado-Cortés, L. L., Forero-Casallas, J. A., & Ruiz-Rosas, V. E. (2021). Artificial vision applied to manufacturing process. Visión Electrónica, 15(1), 113–122. https://doi.org/10.14483/22484728.17432
Johnson, A. (2021). Machine-learning and discrimination: Procedural challenges of algorithmic decision-making. Lund University Publications.
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13, 795-848. https://doi.org/10.1007/s40685-020-00134-w
Konstant, A. (2024). Overcoming bias in a hiring algorithm. In Advances in Medical Technologies and Clinical Practice Book Series (pp. 3). https://doi.org/10.4018/979-8-3693-3226-9.ch003
López Armenta, A., & Sandoval Ceja, M. (2024). El uso de herramientas digitales para mejorar la práctica docente en educación primaria. Enfoque Disciplinario, 9(2), 1-15. https://doi.org/10.70165/enfdis.v9i2.289
Mori, M., Sassetti, S., Cavaliere, V., & Bonti, M. (2024). A systematic literature review on artificial intelligence in recruiting and selection: a matter of ethics. Personnel Review. https://doi.org/10.1108/pr-03-2023-0257
Paramita, D., Okwir, S., & Nuur, C. (2024). Artificial intelligence in talent acquisition: exploring organisational and operational dimensions. International Journal of Organizational Analysis. https://doi.org/10.1108/ijoa-09-2023-3992
Pessach, D., & Shmueli, E. (2021). Improving fairness of artificial intelligence algorithms in privileged-group selection bias data settings. Expert Systems with Applications, 185, 115667. https://doi.org/10.1016/J.ESWA.2021.115667
Poe, R. L., & El Mestari, S. Z. (2024). The conflict between algorithmic fairness and non-discrimination: An analysis of fair automated hiring. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 659015. https://doi.org/10.1145/3630106.3659015
Poe, R., & Mestari, S. (2024). The Conflict Between Algorithmic Fairness and Non-Discrimination: An Analysis of Fair Automated Hiring. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3630106.3659015
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469-481. https://doi.org/10.1145/3351095.3372828
Rao, T. V. N., Stephen, M., Manoj, E., & Sangers, B. (2025). Exploring bias and fairness in machine learning algorithms. In Advances in Computational Intelligence and Robotics Book Series (pp. 14). https://doi.org/10.4018/979-8-3693-5231-1.ch014
Scherer, M. U., King, A. G., & Mrkonich, M. (2019). Applying old rules to new tools: Employment discrimination law in the age of algorithms. Social Science Research Network. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3472805
Singh, R., & Natal, M. (2025). A study on Exploring the Effectiveness of AI-Based Recruitment Tools in Enhancing Talent Acquisition in IT Firms. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0525.1890
Soni, V. (2024). AI in job matching and recruitment: Analyzing the efficiency and equity of automated hiring processes. 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), 10617325. https://doi.org/10.1109/ickecs61492.2024.10617325
Tilmes, N. (2022). Disability, fairness, and algorithmic bias in AI recruitment. Ethics and Information Technology, 24. https://doi.org/10.1007/s10676-022-09633-2
Venkateshwaran, G., Kumar, D., , L., & Devarajulu, V. (2025). Artificial Intelligence in HR: Transforming Recruitment and Selection in IT Industry. Journal of Information Systems Engineering and Management. https://doi.org/10.52783/jisem.v10i17s.2705
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