Intimate partner violence (IPV) is an actual social issue which poses a challenge in terms of prevention, legal action, and reporting the abuse once it has occurred. In this last case, out of the total of female victims that fill a complaint against their abuser and initiate the legal proceedings, a significant number withdraw from it for different reasons. In this field, it is interesting to detect the victims that disengage from the legal process so that professionals can intervene before it occurs. Previous studies have applied statistical models to use input variables and make a prediction of withdrawal. However, it has not been found in the literature any study that uses machine learning models to predict disengagement from the legal proceedings in IPV cases, which can be a better option to detect these events with a higher precision. Therefore, in this work, a novel application of machine learning techniques to predict the decision of victims of IPV to withdraw from prosecution is studied. For this purpose, three different ML algorithms have been optimized and tested with the original dataset to prove the great performance of ML models against non-linear input data. Once the best models have been obtained, explainable artificial intelligence (xAI) techniques have been applied to search for the most informative input features and reduce the original dataset to the most important variables. Finally, these results have been compared to those obtained in the previous work that used statistical techniques, and the set of most informative parameters has been combined with the variables of the previous study, showing that ML-based models have a better predictive accuracy in all cases and that by adding one new variable to the previous work' subset, the accuracy to detect withdrawal improves by 7.5%.
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%.
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