Colaboración humano-IA en reclutamiento: orquestando el ajuste persona–puesto (APP, PJF) y el ajuste persona–organización (APO, POF) para la calidad de la contratación
DOI:
https://doi.org/10.17561/ree.n2.2026.9947Keywords:
IA ética, modelo híbrido de reclutamiento, mitigación de sesgos, ajuste persona–puesto, ajuste persona–organización, análisis asistido por redes neuronalesAbstract
Esta investigación analiza el papel transformador de la inteligencia artificial (IA) en la adquisición de talento, centrándose en su impacto sobre la eficiencia del cribado de candidatos, la alineación entre candidato y puesto, la precisión en la contratación y la reducción de sesgos. Basado en la teoría del ajuste (fit theory), el estudio adopta una metodología cualitativa centrada en directivos. Se realizaron entrevistas semiestructuradas, analizadas mediante un enfoque temático de tres niveles, complementado con un mapeo asistido por redes neuronales para identificar patrones. NVivo 14 apoyó la codificación sistemática y el desarrollo de temas, mientras que Python 3.11, junto con Keras (2.15.0) y TensorFlow (2.14.0), facilitó el análisis de conglomerados y la visualización de relaciones asociadas con el ajuste persona–puesto (PJF) y el ajuste persona–organización (POF).
Los resultados muestran que la IA, respaldada por sólidos marcos de gobernanza, mejora el cribado de currículos, fortalece la correspondencia entre candidatos y puestos y aumenta la equidad en las decisiones de contratación. No obstante, presenta limitaciones para evaluar competencias interpersonales y compatibilidad cultural, lo que resalta la importancia del juicio humano. Se propone un modelo híbrido de reclutamiento en el que la IA evalúa inicialmente el PJF y las evaluaciones humanas estructuradas valoran el POF. Asimismo, se
recomiendan auditorías algorítmicas, prácticas de IA explicable y sólidos estándares de privacidad y rendición de cuentas para promover una integración responsable de la IA.
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