• bias
• data mining
• images
• machine learning
• python library

Many machine learning algorithms are becoming a useful computational tool to find answers to support decisions, but they are strongly criticized for the lack of interpretability in relation to the predictions they provide. In favour of the compromise between accuracy and interpretability, many proposals have been found with many different approaches already developed.
This thesis focuses on the creation of a stable and usable platform that works with some explanation methods existing in literature. According to this goal it is necessary to discover proposed methods dealing with this topic and insert them into a platform. This explainable system gives transparency in the decision policies of some predictive models that might be used by experts and non-experts in the field of machine learning. To support developers, researchers and practitioners, Xlib have to allow easy and standardized access to a wide variety of algorithms integrated into a common framework, to evaluate, compare and visualize the results they provide.