Revelando lo no reportado: extracción de eventos basada en IA para analizar la representación estadounidense de los delitos de odio

Daniel Suárez Alonso

doi:10.12795/IESTSCIENTIA.2024.i01.08

Authors

Daniel Suárez Alonso Miguel Cervantes European University https://orcid.org/0000-0002-4505-2942

DOI:

https://doi.org/10.12795/IESTSCIENTIA.2024.i01.08

Keywords:

Hate crimes, Official reports, Statistical approximations, Event extraction, Multi-instance learning, Artificial intelligence (AI)

Abstract

Official reports of hate crimes in the United States are underestimated compared to the actual number of such incidents. Additionally, despite statistical approximations, many American cities lack official reports on hate incidents. Here, we initially demonstrate that event extraction and multi-instance learning, based on artificial intelligence (AI), applied to a set of local news articles, can predict hate crime cases. We then use the AI-trained model to detect hate incidents in cities for which the FBI lacks Official reports of hate crimes in the United States are underestimated compared to the actual number of such incidents. Additionally, despite statistical approximations, many American cities lack official reports on hate incidents. Here, we initially demonstrate that event extraction and multi-instance learning, based on artificial intelligence (AI), applied to a set of local news articles, can predict hate crime cases. We then use the AI-trained model to detect hate incidents in cities for which the FBI lacks

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