Machine Learning Applied to Gender Violence: A Systematic Mapping Study
Machine Learning (ML) has positioned itself as one of the best tools to address different problems thanks to its data processing capabilities, as well as the different models, algorithms, and predictive factors that help to solve defined problems. Therefore, this article presents a systematic mappin...
| Main Authors: | , , |
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15944 |
| _version_ | 1801706103257432064 |
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| author | Pinto-Muñoz, Cristian-Camilo Zuñiga-Samboni, Jhon-Alex Ordoñez-Erazo, Hugo-Armando |
| author_facet | Pinto-Muñoz, Cristian-Camilo Zuñiga-Samboni, Jhon-Alex Ordoñez-Erazo, Hugo-Armando |
| author_sort | Pinto-Muñoz, Cristian-Camilo |
| collection | OJS |
| description | Machine Learning (ML) has positioned itself as one of the best tools to address different problems thanks to its data processing capabilities, as well as the different models, algorithms, and predictive factors that help to solve defined problems. Therefore, this article presents a systematic mapping from 2018 to 2023 focused on the application of ML to gender-based violence. The methodology followed for this study is based on the definition of elements such as research questions, search strings, bibliographic sources, and inclusion and exclusion criteria. The research results allow us to understand the benefits and challenges of using artificial intelligence, precisely one of its branches, ML, to help combat problems in different areas of society, such as education, health, and violence, among others. It also identifies the countries where ML is being researched and the contexts it is applied to. The study discusses the application of ML to combat gender-based violence. After conducting a literature review, beneficial results were found in the application of artificial intelligence and ML. The results obtained in the different articles showed a predictive capacity and improvements compared to currently used systems. However, despite the positive results, no evidence of the development of an ML model or algorithm applied to gender-based violence in Colombia was found in the review. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-15944 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-159442023-09-03T14:58:44Z Machine Learning Applied to Gender Violence: A Systematic Mapping Study Machine learning aplicado a la violencia de género: un estudio de mapeo sistemático Pinto-Muñoz, Cristian-Camilo Zuñiga-Samboni, Jhon-Alex Ordoñez-Erazo, Hugo-Armando Machine Learning gender-based violence domestic violence Colombia prediction Aprendizaje automático violencia de género violencia intrafamiliar Colombia predicción. Machine Learning (ML) has positioned itself as one of the best tools to address different problems thanks to its data processing capabilities, as well as the different models, algorithms, and predictive factors that help to solve defined problems. Therefore, this article presents a systematic mapping from 2018 to 2023 focused on the application of ML to gender-based violence. The methodology followed for this study is based on the definition of elements such as research questions, search strings, bibliographic sources, and inclusion and exclusion criteria. The research results allow us to understand the benefits and challenges of using artificial intelligence, precisely one of its branches, ML, to help combat problems in different areas of society, such as education, health, and violence, among others. It also identifies the countries where ML is being researched and the contexts it is applied to. The study discusses the application of ML to combat gender-based violence. After conducting a literature review, beneficial results were found in the application of artificial intelligence and ML. The results obtained in the different articles showed a predictive capacity and improvements compared to currently used systems. However, despite the positive results, no evidence of the development of an ML model or algorithm applied to gender-based violence in Colombia was found in the review. Machine Learning (ML) se ha posicionado como una de las mejores herramientas para abordar diferentes problemáticas gracias a su capacidad de procesamiento de datos y a los diferentes modelos, algoritmos y factor predictivo para ayudar a dar solución a los problemas definidos. Es por ello, que este artículo presenta un mapeo sistemático de los años 2018 a 2023, el cual se orienta en la aplicación de ML enfocado en la violencia de género. La metodología seguida para la realización del estudio parte de la definición de elementos, como preguntas de investigación, cadenas de búsqueda, fuentes bibliográficas y criterios de inclusión y exclusión. Los resultados de la investigación permiten comprender los beneficios y retos que presenta el uso de inteligencia artificial, desde específicamente una de sus ramas, el ML, para ayudar a combatir problemas en diferentes ámbitos de la sociedad, como educación, salud, violencia, entre otros. Además de constatar en qué países se está investigando el ML y en qué contextos es aplicado. El trabajo discute la aplicación de ML para combatir la violencia de género. Tras realizar el estudio de revisión de la literatura, se encontraron resultados beneficiosos de la aplicación de inteligencia artificial y ML, ya que los resultados obtenidos en los diferentes artículos presentaban capacidad predictiva y mejoras en comparación con los sistemas actualmente usados. Sin embargo, pese a los resultados positivos, no se encontró en la revisión evidencia de desarrollo de un modelo o algoritmo de ML aplicado a la violencia de género en Colombia. Universidad Pedagógica y Tecnológica de Colombia 2023-06-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15944 10.19053/01211129.v32.n64.2023.15944 Revista Facultad de Ingeniería; Vol. 32 No. 64 (2023): April-June 2023 (Continuous Publication); e15944 Revista Facultad de Ingeniería; Vol. 32 Núm. 64 (2023): Abril-Junio 2023 (Publicación Continua); e15944 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15944/13151 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15944/13450 Copyright (c) 2023 Cristian-Camilo Pinto-Muñoz, Jhon-Alex Zuñiga-Samboni, Hugo-Armando Ordoñez-Erazo http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Machine Learning gender-based violence domestic violence Colombia prediction Aprendizaje automático violencia de género violencia intrafamiliar Colombia predicción. Pinto-Muñoz, Cristian-Camilo Zuñiga-Samboni, Jhon-Alex Ordoñez-Erazo, Hugo-Armando Machine Learning Applied to Gender Violence: A Systematic Mapping Study |
| title | Machine Learning Applied to Gender Violence: A Systematic Mapping Study |
| title_alt | Machine learning aplicado a la violencia de género: un estudio de mapeo sistemático |
| title_full | Machine Learning Applied to Gender Violence: A Systematic Mapping Study |
| title_fullStr | Machine Learning Applied to Gender Violence: A Systematic Mapping Study |
| title_full_unstemmed | Machine Learning Applied to Gender Violence: A Systematic Mapping Study |
| title_short | Machine Learning Applied to Gender Violence: A Systematic Mapping Study |
| title_sort | machine learning applied to gender violence a systematic mapping study |
| topic | Machine Learning gender-based violence domestic violence Colombia prediction Aprendizaje automático violencia de género violencia intrafamiliar Colombia predicción. |
| topic_facet | Machine Learning gender-based violence domestic violence Colombia prediction Aprendizaje automático violencia de género violencia intrafamiliar Colombia predicción. |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15944 |
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