Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD

Autores:: Romero-Luna, Roxana-María
Ordoñez-Erazo, Hugo-Armando
Cobos-Lozada, Carlos-Alberto

Tipo de recurso:: Article of journal

Fecha de publicación:: 2026

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:: eng

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dc.title.spa.fl_str_mv	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
title	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
spellingShingle	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD class differentiation child labor local explainability multiclass classification clasificación multiclase diferenciación de clases explicabilidad local trabajo infantil
title_short	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
title_full	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
title_fullStr	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
title_full_unstemmed	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
title_sort	Local Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCD
dc.creator.fl_str_mv	Romero-Luna, Roxana-María Ordoñez-Erazo, Hugo-Armando Cobos-Lozada, Carlos-Alberto
dc.contributor.author.eng.fl_str_mv	Romero-Luna, Roxana-María Ordoñez-Erazo, Hugo-Armando Cobos-Lozada, Carlos-Alberto
dc.subject.none.fl_str_mv	class differentiation child labor local explainability multiclass classification clasificación multiclase diferenciación de clases explicabilidad local trabajo infantil
topic	class differentiation child labor local explainability multiclass classification clasificación multiclase diferenciación de clases explicabilidad local trabajo infantil
publishDate	2026
dc.date.accessioned.none.fl_str_mv	2026-11-03 14:55:00 2026-11-05T10:45:11Z
dc.date.available.none.fl_str_mv	2026-11-03 14:55:00
dc.date.issued.none.fl_str_mv	2026-11-03
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.19053/uptc.01211129.v35.n77.2026.21709
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dc.language.iso.eng.fl_str_mv	eng
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dc.relation.references.eng.fl_str_mv	Organización Internacional del Trabajo (OIT), Trabajo infantil, 2026. https://www.ilo.org/es/temas/trabajo-infantil/que-se-entiende-por-trabajo-infantil Oficina Internacional del Trabajo y Fondo de las Naciones Unidas para la Infancia, Trabajo infantil: Estimaciones mundiales 2024, tendencias y el camino a seguir – Resumen, 2025. https://www.unicef.es/sites/unicef.es/files/communication/2024-trabajo-infantil-resumen.pdf Departamento Administrativo Nacional de Estadística (DANE), Mercado laboral-Trabajo infantil, 2026. https://www.dane.gov.co/files/operaciones/GEIH/bol-GEIHTI-oct-dic2025.pdf Asamblea Nacional Constituyente, Constitución política de Colombia de 1991," Sistema Único de Información Normativa-SUIN-Juriscol, 2025. https://www.mininterior.gov.co/normativas/constitucion-politica-de-colombia/ Congreso de Colombia, Ley 1098 de 2006. Por la cual se expide el Código de la Infancia y la Adolescencia, 2006. https://www.funcionpublica.gov.co/eva/gestornormativo/norma.php?i=22106 E. B. Chocobar, Prediciendo trabajo infantil: comparación de técnicas de econometría tradicional y machine learning, Grade Thesis, Universidad de San Andrés, Dec. 2021. https://dspaceapi.live.udesa.edu.ar/server/api/core/bitstreams/89f6b638-b3a4-42df-b6ef-a4fe99d577c1/content N. T. Zeini, P. M. Fikry, "Leveraging machine learning and resampling techniques to analyze contributing factors to child labor in Egypt," Journal of Computational Social Science, vol. 8, no. 4, e87, Nov. 2025. https://doi.org/10.1007/s42001-025-00424-5 N. A. Wani, R. Kumar, J. Bedi, "DeepXplainer: An interpretable deep learning-based approach for lung cancer detection using explainable artificial intelligence," Computer Methods and Programs in Biomedicine, vol. 243, e107879, Jan. 2024. https://doi.org/10.1016/j.cmpb.2023.107879 T. T. H. Le, R. W. Wardhani, D. S. Catur Putranto, U. Jo, H. Kim, "Toward enhanced attack detection and explanation in intrusion detection system-based IoT environment data," IEEE Access, vol. 11, pp. 131661-131676, 2023. https://doi.org/10.1109/ACCESS.2023.3336678 S. Kang, G. Kim, C. D. Yoo, "Fair facial attribute classification via causal graph-based attribute translation," Sensors, vol. 22, no. 14, e5271, Jul. 2022. https://doi.org/10.3390/s22145271 B. Janssens, L. Schetgen, M. Bogaert, M. Meire, D. Van den Poel, "360 degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, vol. 317, no. 2, pp. 366-381, Sep. 2024. https://doi.org/10.1016/j.ejor.2023.06.024 P. Przybyła, A. J. Soto, "When classification accuracy is not enough: Explaining news credibility assessment," Information Processing and Management, vol. 58, no. 5, e102653, Sep. 2021. https://doi.org/10.1016/j.ipm.2021.102653 X. Dastile, T. Celik, "Counterfactual explanations with multiple properties in credit scoring," IEEE Access, vol. 12, pp. 110713-110728, 2024. https://doi.org/10.1109/ACCESS.2024.3441037 D. Zheng et al., "Explainable deep learning for automatic rock classification," Computers and Geosciences, vol. 184, e105511, Feb. 2023. https://doi.org/10.1016/j.cageo.2023.105511 A. Barredo Arrieta et al., "Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82-115, Jun. 2020. https://doi.org/10.1016/j.inffus.2019.12.012 F. Pistolesi, M. Baldassini, E. Volpe, F. Focacci, E. Cattoni, "Fast and interpretable prediction of seismic kinematics of flexible retaining walls in sand through explainable artificial intelligence," Computers and Geotechnics, vol. 179, e107007, Mar. 2025. https://doi.org/10.1016/j.compgeo.2024.107007 R. M. B. de Oliveira, D. Martens, "A framework and benchmarking study for counterfactual generating methods on tabular data," Applied Sciences, vol. 11, no. 16, e7274, Aug. 2021. https://doi.org/10.3390/app11167274 S. Gawde, S. Patil, S. Kumar, P. Kamat, K. Kotecha, "An explainable predictive maintenance strategy for multi-fault diagnosis of rotating machines using multi-sensor data fusion," Decision Analytics Journal, vol. 10, e100425, Mar. 2024. https://doi.org/10.1016/j.dajour.2024.100425 A. Artelt, F. Hinder, V. Vaquet, R. Feldhans, B. Hammer, "Contrasting explanations for understanding and regularizing model adaptations," Neural Processing Letters, vol. 55, no. 5, pp. 5273-5297, Oct. 2023. https://doi.org/10.1007/s11063-022-10826-5 I. Stepin, J. M. Alonso-Moral, A. Catala, M. Pereira-Fariña, "An empirical study on how humans appreciate automated counterfactual explanations which embrace imprecise information," Information Sciences, vol. 618, pp. 379-399, Dec. 2022. https://doi.org/10.1016/j.ins.2022.10.098 M. de Sousa Balbino, L. E. Z. Gálvez, C. N. Nobre, "CSSE - An agnostic method of counterfactual, selected, and social explanations for classification models," Expert Systems with Applications, vol. 228, e120373, Oct. 2023. https://doi.org/10.1016/j.eswa.2023.120373 P. J. G. Lisboa, S. Saralajew, A. Vellido, R. Fernández-Domenech, T. Villmann, "The coming of age of interpretable and explainable machine learning models," Neurocomputing, vol. 535, pp. 25-39, May 2023. https://doi.org/10.1016/j.neucom.2023.02.040 N. 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Lozada, "Shapley additive explanation for local class differentiation: Local explainability for class differentiation in classification models," Advanced Intelligent Systems, vol. 8, ee70397, Apr. 2026. https://doi.org/10.1002/aisy.70397 R. Romero, H. Ordoñez, C. Cobos, "Explainability approaches for class differentiation in classification models: A systematic review," AI, vol. 7, no. 6, e203, Jun. 2026. https://doi.org/10.3390/ai7060203 Y. han Sheu, C. Magdamo, M. Miller, S. Das, D. Blacker, J. W. Smoller, "AI-assisted prediction of differential response to antidepressant classes using electronic health records," Digital Medicine, vol. 6, no. 1, e817, Dec. 2023. https://doi.org/10.1038/s41746-023-00817-8 Y. Nasir, K. Kadian, A. Sharma, V. Dwivedi, "Interpretable machine learning for dermatological disease detection: Bridging the gap between accuracy and explainability," Computers in Biology and Medicine, vol. 179, e108919, Sep. 2024. https://doi.org/10.1016/j.compbiomed.2024.108919 F. Khanom, S. Biswas, M. S. Uddin, R. Mostafiz, "XEMLPD: An explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features," International Journal of Speech Technology, vol. 27, no. 4, pp. 1055-1083, Dec. 2024. https://doi.org/10.1007/s10772-024-10152-2 M. Dabass, S. Vashisth, R. Vig, "A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images," Computers in Biology and Medicine, vol. 147, e105680, Aug. 2022. https://doi.org/10.1016/j.compbiomed.2022.105680 K. Sokol, P. Flach, "One explanation does not fit all: The promise of interactive explanations for machine learning transparency," Künstliche Intelligenz, vol. 34, no. 2, pp. 235-250, Jun. 2020. https://doi.org/10.1007/s13218-020-00637-y J. Hatwell, M. M. Gaber, R. Muhammad Atif Azad, "Gbt-hips: Explaining the classifications of gradient boosted tree ensembles," Applied Sciences, vol. 11, no. 6, e2511, Mar. 2021. https://doi.org/10.3390/app11062511 G. Louppe, L. Wehenkel, A. Sutera, P. Geurts, "Understanding variable importances in forests of randomized trees," in Advances in Neural Information Processing Systems, 2013. https://proceedings.neurips.cc/paper_files/paper/2013/file/e3796ae838835da0b6f6ea37bcf8bcb7-Paper.pdf B. Badhon, R. K. Chakrabortty, S. G. Anavatti, M. Vanhoucke, "A multi-module explainable artificial intelligence framework for project risk management: Enhancing transparency in decision-making," Engineering Applications of Artificial Intelligence, vol. 148, e110427, May 2025. https://doi.org/10.1016/j.engappai.2025.110427 Dirección de Metodología y Producción Estadística (DIMPE), Encuesta Nacional de Calidad de Vida – ECV, 2022. https://microdatos.dane.gov.co/index.php/catalog/793/data-dictionary/F21?file_name=Servicios%20del%20hogar# R. Romero L., H. Ordoñez E., C. Cobos L., "IA aplicada a la identificación de características de ocupación de menores en hogares colombianos para detectar posible trabajo infantil," Revista Científica, vol. 50, no. 2, pp. 55-70, 2024, https://doi.org/10.14483/23448350.22623 C. Molnar, Limitations of Interpretable Machine Learning Methods, 2019. https://github.com/compstat-lmu/iml_methods_limitations A. Cottin, M. Zulian, N. Pécuchet, A. Guilloux, S. 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dc.rights.eng.fl_str_mv	Roxana-María Romero-Luna, Hugo-Armando Ordoñez-Erazo, Carlos-Alberto Cobos-Lozada - 2026
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spelling	Romero-Luna, Roxana-MaríaOrdoñez-Erazo, Hugo-ArmandoCobos-Lozada, Carlos-Alberto2026-11-03 14:55:002026-11-05T10:45:11Z2026-11-03 14:55:002026-11-030121-1129https://repositorio.uptc.edu.co/handle/001/2340110.19053/uptc.01211129.v35.n77.2026.217092357-5328https://doi.org/10.19053/uptc.01211129.v35.n77.2026.21709application/pdfengUniversidad Pedagógica y Tecnológica de ColombiaRoxana-María Romero-Luna, Hugo-Armando Ordoñez-Erazo, Carlos-Alberto Cobos-Lozada - 2026https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/21709class differentiationchild laborlocal explainabilitymulticlass classificationclasificación multiclasediferenciación de clasesexplicabilidad localtrabajo infantilLocal Class Differentiation in the Occupation of Minors in Colombian Households: A Comparison of Explainability Results from SHAP and SHAP-LCDArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Organización Internacional del Trabajo (OIT), Trabajo infantil, 2026. https://www.ilo.org/es/temas/trabajo-infantil/que-se-entiende-por-trabajo-infantilOficina Internacional del Trabajo y Fondo de las Naciones Unidas para la Infancia, Trabajo infantil: Estimaciones mundiales 2024, tendencias y el camino a seguir – Resumen, 2025. https://www.unicef.es/sites/unicef.es/files/communication/2024-trabajo-infantil-resumen.pdfDepartamento Administrativo Nacional de Estadística (DANE), Mercado laboral-Trabajo infantil, 2026. https://www.dane.gov.co/files/operaciones/GEIH/bol-GEIHTI-oct-dic2025.pdfAsamblea Nacional Constituyente, Constitución política de Colombia de 1991," Sistema Único de Información Normativa-SUIN-Juriscol, 2025. https://www.mininterior.gov.co/normativas/constitucion-politica-de-colombia/Congreso de Colombia, Ley 1098 de 2006. Por la cual se expide el Código de la Infancia y la Adolescencia, 2006. https://www.funcionpublica.gov.co/eva/gestornormativo/norma.php?i=22106E. B. Chocobar, Prediciendo trabajo infantil: comparación de técnicas de econometría tradicional y machine learning, Grade Thesis, Universidad de San Andrés, Dec. 2021. https://dspaceapi.live.udesa.edu.ar/server/api/core/bitstreams/89f6b638-b3a4-42df-b6ef-a4fe99d577c1/contentN. T. Zeini, P. M. Fikry, "Leveraging machine learning and resampling techniques to analyze contributing factors to child labor in Egypt," Journal of Computational Social Science, vol. 8, no. 4, e87, Nov. 2025. https://doi.org/10.1007/s42001-025-00424-5N. A. Wani, R. Kumar, J. Bedi, "DeepXplainer: An interpretable deep learning-based approach for lung cancer detection using explainable artificial intelligence," Computer Methods and Programs in Biomedicine, vol. 243, e107879, Jan. 2024. https://doi.org/10.1016/j.cmpb.2023.107879T. T. H. Le, R. W. Wardhani, D. S. Catur Putranto, U. Jo, H. Kim, "Toward enhanced attack detection and explanation in intrusion detection system-based IoT environment data," IEEE Access, vol. 11, pp. 131661-131676, 2023. https://doi.org/10.1109/ACCESS.2023.3336678S. Kang, G. Kim, C. D. Yoo, "Fair facial attribute classification via causal graph-based attribute translation," Sensors, vol. 22, no. 14, e5271, Jul. 2022. https://doi.org/10.3390/s22145271B. Janssens, L. Schetgen, M. Bogaert, M. Meire, D. Van den Poel, "360 degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, vol. 317, no. 2, pp. 366-381, Sep. 2024. https://doi.org/10.1016/j.ejor.2023.06.024P. Przybyła, A. J. Soto, "When classification accuracy is not enough: Explaining news credibility assessment," Information Processing and Management, vol. 58, no. 5, e102653, Sep. 2021. https://doi.org/10.1016/j.ipm.2021.102653X. Dastile, T. Celik, "Counterfactual explanations with multiple properties in credit scoring," IEEE Access, vol. 12, pp. 110713-110728, 2024. https://doi.org/10.1109/ACCESS.2024.3441037D. Zheng et al., "Explainable deep learning for automatic rock classification," Computers and Geosciences, vol. 184, e105511, Feb. 2023. https://doi.org/10.1016/j.cageo.2023.105511A. Barredo Arrieta et al., "Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82-115, Jun. 2020. https://doi.org/10.1016/j.inffus.2019.12.012F. Pistolesi, M. Baldassini, E. Volpe, F. Focacci, E. Cattoni, "Fast and interpretable prediction of seismic kinematics of flexible retaining walls in sand through explainable artificial intelligence," Computers and Geotechnics, vol. 179, e107007, Mar. 2025. https://doi.org/10.1016/j.compgeo.2024.107007R. M. B. de Oliveira, D. Martens, "A framework and benchmarking study for counterfactual generating methods on tabular data," Applied Sciences, vol. 11, no. 16, e7274, Aug. 2021. https://doi.org/10.3390/app11167274S. Gawde, S. Patil, S. Kumar, P. Kamat, K. Kotecha, "An explainable predictive maintenance strategy for multi-fault diagnosis of rotating machines using multi-sensor data fusion," Decision Analytics Journal, vol. 10, e100425, Mar. 2024. https://doi.org/10.1016/j.dajour.2024.100425A. Artelt, F. Hinder, V. Vaquet, R. Feldhans, B. Hammer, "Contrasting explanations for understanding and regularizing model adaptations," Neural Processing Letters, vol. 55, no. 5, pp. 5273-5297, Oct. 2023. https://doi.org/10.1007/s11063-022-10826-5I. Stepin, J. M. Alonso-Moral, A. Catala, M. Pereira-Fariña, "An empirical study on how humans appreciate automated counterfactual explanations which embrace imprecise information," Information Sciences, vol. 618, pp. 379-399, Dec. 2022. https://doi.org/10.1016/j.ins.2022.10.098M. de Sousa Balbino, L. E. Z. Gálvez, C. N. Nobre, "CSSE - An agnostic method of counterfactual, selected, and social explanations for classification models," Expert Systems with Applications, vol. 228, e120373, Oct. 2023. https://doi.org/10.1016/j.eswa.2023.120373P. J. G. Lisboa, S. Saralajew, A. Vellido, R. Fernández-Domenech, T. Villmann, "The coming of age of interpretable and explainable machine learning models," Neurocomputing, vol. 535, pp. 25-39, May 2023. https://doi.org/10.1016/j.neucom.2023.02.040N. 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