Abstract: Techniques are described herein that are capable of generating a content signature of a textual communication using OCR and text processing. The textual communication is rendered. Text is extracted from the rendered textual communication using OCR. Customization is removed from the text to provide a templatized version of the rendered textual communication that includes de-customized text. The de-customized text is parsed into tokens. Each token includes a respective subset of a plurality of characters. The tokens are converted into respective numbers. Each number is processed using fuzzy hash functions to provide respective hash values associated with the respective token. Representative hash values are selected for the respective fuzzy hash functions by selecting each representative hash value from the hash values that are processed using the respective fuzzy hash function.

Abstract: In systems for interpreting the predictions of a machine learning model with the help of a surrogate model, feature vectors of inputs to the machine learning model can be grouped based on locality sensitive hashes or other hashes that reflect similarity between the feature vectors in matching hash values. For a given prediction to be interpreted and the corresponding input feature vector, a suitable training dataset for the surrogate model can then be obtained at low computational cost by hashing the input feature vector and retrieving stored feature vectors with matching hash values, along with their respective predictions.