Diversity and inclusion strategies in AI-assisted recruitment processes
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The study critically explores the impact of artificial intelligence on recruitment processes, focusing on its ability to reproduce historical biases that affect employment diversity. Through a literature review of research and policy documents between 2013 and 2024, strategies aimed at mitigating algorithmic bias were identified, including fairness audits, use of balanced data, de-biasing techniques, and multidisciplinary team building. The findings reveal that no single measure is sufficient and that effective deployment requires a combination of technical practices and organizational frameworks committed to transparency and equity. It is concluded that the responsible adoption of AI in recruitment depends on articulating technological innovation with sound ethical principles, emphasizing the need for inclusive policies, auditable processes and the integration of diverse perspectives to ensure equitable opportunities in talent selection.
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Alameda Castillo, M. T. (2021). Reclutamiento tecnológico: Sobre algoritmos y acceso al empleo. Temas laborales: Revista andaluza de trabajo y bienestar social, (159), 11–52. https://dialnet.unirioja.es/servlet/articulo?codigo=8250088
Bravo-Bravo, I. F. (2023). Aplicación de blockchain en la trazabilidad de la cadena de suministro. Revista Científica Ciencia Y Método, 1(2), 1-14. https://doi.org/10.55813/gaea/rcym/v1/n2/11 DOI: https://doi.org/10.55813/gaea/rcym/v1/n2/11
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. En Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 77–91). Proceedings of Machine Learning Research, 81. https://proceedings.mlr.press/v81/buolamwini18a.html
Calvard, T. S., & Jeske, D. (2018). Reporting and recording bias and discrimination in artificial intelligence recruitment. Employee Relations, 40(6), 1024–1038.
Casanova-Villalba, C. I., Herrera-Sánchez, M. J., Lopez-Pincay, P. R., & Rivera-Guerrero, A. L. (2024). Transparencia contable y su relación con la confianza de los inversionistas. Innova Science Journal, 2(2), 1-14. https://doi.org/10.63618/omd/isj/v2/n2/34 DOI: https://doi.org/10.63618/omd/isj/v2/n2/34
Casanova-Villalba, C. I., Herrera-Sánchez, M. J., Rivadeneira-Moreira, J. C., Ramos-Secaira, F. M., & Bueno-Moyano, F. R. (2022). Modelo Kaizen en el sector público. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.2022.8 DOI: https://doi.org/10.55813/egaea.l.2022.8
Colina-Vargas, A. M., Espinoza-Mina, M. A., & Silva-Garcés, J. F. (2024). Dinámicas y tendencias de la ciencia ciudadana en América Latina y el Caribe: Un análisis bibliométrico y temático. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.92 DOI: https://doi.org/10.55813/egaea.l.92
Espinosa Vera, A. J., & Mendoza Vargas, E. Y. (2024). Innovación tecnológica para el desarrollo de los emprendimientos sociales. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.85 DOI: https://doi.org/10.55813/egaea.l.85
European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts (COM(2021) 206 final) [Proposal]. EUR-Lex. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206
Ferrara, E. (2024). Fairness and bias in artificial intelligence: A brief survey of sources, impacts, and mitigation strategies. Sci, 6(1), 3. https://doi.org/10.3390/sci6010003 DOI: https://doi.org/10.3390/sci6010003
Flores-Murillo, C. R., Hoffmann-Jaramillo, K., Vallejo-López, A. B., & Ana María, S.-R. (2024). Una ventana abierta hacia la calidad total en un mundo globalizado. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.73 DOI: https://doi.org/10.55813/egaea.l.73
Galarza-Sánchez, P. C., Boné-Andrade, M. F., & Pinargote-Bravo, V. J. (2023). Aplicaciones de inteligencia artificial generativa en la transformación digital empresarial. Revista Científica Ciencia Y Método, 1(1), 28-41. https://doi.org/10.55813/gaea/rcym/v1/n1/8 DOI: https://doi.org/10.55813/gaea/rcym/v1/n1/8
Herrera-Sánchez, M. J., Geovely Jaritza, O. J., Quezada Valarezo, Y. D., Rivas Bravo, A. L., Navarrete-Zambrano, C. M., Boné-Andrade, M. F., Parraga-Pether, P. V., Alcívar Vélez, J., Karina Auxiliadora, S. M., Cabrera Aguilar, J. K., Zambrano Flores, P. A., Puyol-Cortez, J. L., Guevara Salcedo, W. A., Urgiles Medina, E. A., Pilatasig Vivanco, M. C., López-Pérez, P. J., Moreira Mendoza, M. B., Vélez Solorzano, B. X., Zambrano Rodríguez, L. A., … Solórzano Vélez, H. V. (2022). Análisis Científico de la Ética desde la Perspectiva Multidisciplinaria. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.2022.13 DOI: https://doi.org/10.55813/egaea.l.2022.13
Kamiran, F., & Calders, T. (2012). Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1), 1–33. https://doi.org/10.1007/s10115-011-0463-8 DOI: https://doi.org/10.1007/s10115-011-0463-8
Kim, P. T. (2022). Data-driven discrimination at work. William & Mary Law Review, 63(1), 1–64.
Kuhlman, C., Jackson, L., & Chunara, R. (2020). No computation without representation: Avoiding data and algorithm biases through diversity. arXiv preprint arXiv:2002.11836. https://doi.org/10.48550/arXiv.2002.11836 DOI: https://doi.org/10.1145/3394486.3411074
Macias-Loor, J. M., Prado-Chinga, A. E., Alcívar-Soria, E. E., Solano-Gutiérrez, G. A., Quisaguano-Calo, M. F., Cruel-Preciado, M., Endara-Arguello, I. O., Bonilla-Bonilla, M. A., Paredes-Álvarez, P. L., Murillo-Cusme, M. A., Pita-González, G. D., Márquez-Montenegro, K. A., Lirio-Moncayo, V. Y., Riofrio-Casa, D. M., Canchingre-Suárez, K. M., Corozo-Villacis, M. L., Moreira-Mera, J. V., Angulo-Quiñonez, A. D., Vergara-Barragan, J. E., Romero-Pin, M. B., Aviles-Barre, D. A., Quezada-Mazamba, S. N., Rugel-Calderón, F. J., Macías-Alcívar, J. S., Yanchaguano-Loor, A. N., Bone-Quiñonez, E. A., Casanova-Villalba, C. I., Herrera-Sánchez, M. J., & Casanova-Villalba, L. A. (2024). Gestión Inteligente Sinergias en la Administración. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.106 DOI: https://doi.org/10.55813/egaea.l.106
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), Article 115. https://doi.org/10.1145/3457607 DOI: https://doi.org/10.1145/3457607
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 DOI: https://doi.org/10.1145/3351095.3372828
Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 429–435. https://doi.org/10.1145/3306618.3314244 DOI: https://doi.org/10.1145/3306618.3314244
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Hutchinson, B., Gebru, T., ... & Barnes, P. (2020). Closing the AI accountability gap: Defining an end to end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20), 33–44. https://doi.org/10.1145/3351095.3372873 DOI: https://doi.org/10.1145/3351095.3372873
Rieke, A., & Bogen, M. (2018). Help wanted: An examination of hiring algorithms, equity, and bias. Upturn. https://www.upturn.org/reports/2018/hiring-algorithms/
Romero-Reyes, H. D., & Paladines-Torres, L. K. (2024). Análisis Comparativo entre Instrumentos de Evaluación de Clima Laboral y la Evaluación ECLO. Journal of Economic and Social Science Research, 4(2), 197–213. https://doi.org/10.55813/gaea/jessr/v4/n2/107 DOI: https://doi.org/10.55813/gaea/jessr/v4/n2/107
Saleiro, P., Kuester, B., Hinkson, L., London, J., et al. (2018). Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577. https://doi.org/10.48550/arXiv.1811.05577
Vallejo-Rosero, C. A., Quesada-Paz, M. M., Londoño-Puentes, J. C., & López-Contreras, J. del R. (2024). Factores que afectan el desempeño laboral en el personal de la salud en una IPS de primer nivel en el municipio de Candelaria Valle del Cauca: un análisis para el primer semestre del 2023. Journal of Economic and Social Science Research, 4(2), 114–139. https://doi.org/10.55813/gaea/jessr/v4/n2/97 DOI: https://doi.org/10.55813/gaea/jessr/v4/n2/97
van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215–222. https://doi.org/10.1016/j.chb.2018.09.009 DOI: https://doi.org/10.1016/j.chb.2018.09.009
Vernaza-Arroyo, G. D., Mina Bone, S. G., Flores Alvarado, E. A., Rueda Orozco, G. J., Zambrano Vélez, D. L., Casanova-Villalba, C. I., Intriago Sánchez, J. E., Molina Valdez, L. A., Moreira Vera, N. C., Proaño-Gonzalez, E. A., Escobar Quiña, J. D., Gómez Pacheco, M. I., Cruz Campos, D. C., Salgado Ortiz, P. J., Avilés Bravo, V. I., Espín Chila, D. M., Fuentes Mora, A. M., Ruiz Zambrano, L. G., Benavidez Mendoza, M. G., Pin Zamora, L. F. (2022). Resultados Científicos de la Investigación Multidisciplinaria desde la Perspectiva Ética. Editorial Grupo AEA. https://doi.org/10.55813/egaea.l.2022.14 DOI: https://doi.org/10.55813/egaea.l.2022.14