A Survey of Methods for Explaining Black Box Models
MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable
An Evaluation of the Human-Interpretability of Explanation
Interpretation of Neural Networks is Fragile
Methods for Interpreting and Understanding Deep Neural Networks
A causal framework for explaining the predictions of black-box sequence-to-sequence models
Fibres of Failure: Classifying errors in predictive processes
Explaining Explanations in AI
Integrated Commonsense Reasoning and Probabilistic Planning
RISE: Randomized Input Sampling for Explanation of Black-box Models
A survey on instance selection for active learning