Understanding the Criminal Behavior in Mexico City through an Explainable Artificial Intelligence Model

Nowadays, the Mexican government is showing a great interest in decreasing the crime rate in Mexico. A way to carry out this task is to understand criminal behavior in each Mexico states by using an eXplainable Artificial Intelligence (XAI) model. In this paper, we propose to understand the criminal behavior of the Mexico city by using an XAI model jointly with our proposed feature representation based on the weather. Our experimental results show how our proposed feature representation allows for improving all tested classifiers. Also, we show that the XAI-based classifier improves other tested state-of-the-art classifiers.

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