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Programs as Black-Box Explanations
Sameer Singh, Marco Tulio Ribeiro, Carlos Guestrin
Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or m…
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three p…
Model-Agnostic Interpretability of Machine Learning
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to tru…