A twist in intimate partner violence risk assessment tools: gauging the contribution of exogenous and historical variables
Gender violence is a problem that affects millions of people worldwide. Among its many manifestations Intimate Partner Violence (IPV) is one of the most common. In Spain, a police monitoring protocol has been developed to minimize recidivism in IPV cases. This protocol is complemented by VioGén, an...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/717610 |
| Acceso en línea: | http://hdl.handle.net/10486/717610 https://dx.doi.org/10.1016/j.knosys.2021.107586 |
| Access Level: | acceso abierto |
| Palabra clave: | Police Risk Assessment Reassault Risk Assessment Machine Learning Viogén System Intimate Partner Violence Gender Violence Informática |
| Sumario: | Gender violence is a problem that affects millions of people worldwide. Among its many manifestations Intimate Partner Violence (IPV) is one of the most common. In Spain, a police monitoring protocol has been developed to minimize recidivism in IPV cases. This protocol is complemented by VioGén, an Intimate Partner Violence Risk Assessment Tool (IPVRAT) created by the Spanish State Secretariat for Security of the Ministry of Interior (SES) for risk prediction. VioGén’s goal is to help the authorities determine what security and safety measures are most suitable. This paper improves on the current version of VioGén by introducing a model based on machine learning and data science and by studying the predictive value of exogenous and historical variables. The model is fitted on an anonymized database provided by SES and extracted from VioGén. This database includes the 2-year evolution of 46,047 new cases of IPV violence reported between October 2016 and December 2017, making it the largest database analyzed in the field. Obtained results show a clear improvement in the predictive capabilities of the new model against the original system, where it would have corrected more than 25% of the infra-protected cases, while improving the overall accuracy at the same time. Finally, lessons learned from the performed study and experiments are reported to aid in the design of future IPVRAT. In particular, insights show that IPVRAT should not treat cases statically as the incorporation of information regarding their evolution improves significantly the model’s performance |
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