Abstract
A common and recently widely accepted problem in the field of machine learning is the black box nature of many algorithms. In practice, machine learning algorithms are typically being viewed in terms of their inputs and outputs, but without any knowledge of their internal workings. Perhaps the most notorious examples in this context are artificial neural networks and deep learning techniques, but they are certainly not the only techniques that suffer from this problem. Matrix factorisation models for recommendation systems, for example, suffer from the same lack of interpretability. Our research focuses on applying and adapting pattern mining techniques to gain meaningful insights in big data algorithms by analyzing them in terms of both their input and output, also allowing us to compare different algorithms and discover the hidden biases that lead to those differences.
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