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With the increased usage of artificial intelligence (AI), it is imperative tounderstand how these models work internally. These needs have led to thedevelopment of a new field called eXplainable artificial intelligence (XAI).This field consists of on a set of techniques that allows us to theoreticallydetermine the cause of the AI decisions. One main issue of XAI is how to verifythe works on this field, taking into consideration the lack of ground truth(GT). In this study, we propose a new method to generate datasets with GT. Weconducted a set of experiments that compared our GT with real modelexplanations and obtained excellent results confirming that our proposed methodis correct.

A novel approach to generate datasets with XAI ground truth to evaluate image models