Explainable Debugger for Black-box Machine Learning Models

The research around developing methods for debugging and refining Machine Learning (ML) models is still in its infancy. We believe employing tailored tools in the development process can help developers in creating more trustworthy and reliable models. This is particularly essential for creating black-box models such as deep neural networks and random forests, as their opaque decision-making and complex structure prevent detailed investigations. Although many explanation techniques provide interpretability in terms of predictive features for a mispredicted instance, it would be beneficial for a developer to find a partition of the training data that significantly influences the anomaly. Such responsible partitions can be subjected to data visualization and data engineering in the development phase to improve the model's accuracy. In this paper, we propose a systematic debugging framework for the development of ML models that guides the data engineering process using the model's decision boundary. Our approach finds the influential neighborhood of anomalous data points using observation-level feature importance and explains them via a novel quasi-global explanation technique. It is also equipped with a robust global explanation approach to reveal general trends and expose potential biases in the neighborhoods. We demonstrate the efficacy of the devised framework through several experiments on standard data sets and black-box models and propose various guidelines on how the framework's components can be practically useful from a developer's perspective.