Interpreting Shared Deep Learning Models via Explicable Boundary Trees

Understand

Despite outperforming the human in many tasks, deep neural network models are also criticized for the lack of transparency and interpretability in decision making.

• The opaqueness results in uncertainty and low confidence when deploying such a model in model sharing scenarios, when the model is developed by a third party.
• For a supervised machine learning model, sharing training process including training data provides an effective way to gain trust and to better understand model predictions.
• However, it is not always possible to share all training data due to privacy and policy constraints.