Towards Explainable AI with Feature Space Exploration

Neural networks trained on large amounts of data have led to incredible technological leaps affecting nearly every part of our lives. These advances have come at a cost -- namely the interpretability and explainability of data models. Corresponding with the complexity of the operation, the criteria for "choosing" a given output for an input becomes rather mysterious, leading some to refer to neural networks as a "black box" method. Deep neural networks work so marvelously because they learn efficient representations of data, and they are intentionally constrained to capture complex, non-linear patterns in the data. The trade off of recognizing non-linear patterns is comparable to losing the sense of sight, only to gain a more subtle perception of sound.