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Deep learning models trained using massive amounts of data tend to captureone view of the data and its associated mapping. Different deep learning modelsbuilt on the same training data may capture different views of the data basedon the underlying techniques used. For explaining the decisions arrived byblackbox deep learning models, we argue that it is essential to reproduce thatmodel's view of the training data faithfully. This faithful reproduction canthen be used for explanation generation. We investigate two methods for dataview extraction: hill-climbing approach and a GAN-driven approach. We then usethis synthesized data for creating shadow models for explanation generation:Decision-Tree model and Formal Concept Analysis based model. We evaluate theseapproaches on a Blackbox model trained on public datasets and show itsusefulness in explanation generation.

Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation