Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain.

Autor: Escobar-Linero E; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, Spain., García-Jiménez M; Department of Experimental Psychology, Facultad de Psicología, C/Camilo José Cela s/n, Universidad de Sevilla, Seville, Spain., Trigo-Sánchez ME; Department of Experimental Psychology, Facultad de Psicología, C/Camilo José Cela s/n, Universidad de Sevilla, Seville, Spain., Cala-Carrillo MJ; Department of Experimental Psychology, Facultad de Psicología, C/Camilo José Cela s/n, Universidad de Sevilla, Seville, Spain., Sevillano JL; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, Spain., Domínguez-Morales M; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, Spain.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Jun 07; Vol. 18 (6), pp. e0276032. Date of Electronic Publication: 2023 Jun 07 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0276032
Abstrakt: Intimate partner violence against women (IPVW) is a pressing social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. However, a significant number of female victims who file a complaint against their abuser and initiate legal proceedings, subsequently, withdraw charges for different reasons. Research in this field has been focusing on identifying the factors underlying women victims' decision to disengage from the legal process to enable intervention before this occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, none have used machine learning models to predict disengagement from legal proceedings in IPVW cases. This could represent a more accurate way of detecting these events. This study applied machine learning (ML) techniques to predict the decision of IPVW victims to withdraw from prosecution. Three different ML algorithms were optimized and tested with the original dataset to assess the performance of ML models against non-linear input data. Once the best models had been obtained, explainable artificial intelligence (xAI) techniques were applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results were compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters was combined with the variables of the previous study, showing that ML-based models had a better predictive accuracy in all cases and that by adding one new variable to the previous work's predictive model, the accuracy to detect withdrawal improved by 7.5%.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Escobar-Linero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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