SOTAVerified

Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 511–520 of 537 papers

TitleStatusHype
Gaining Free or Low-Cost Transparency with Interpretable Partial SubstituteCode0
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine LearningCode0
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning—0
The Promise and Peril of Human Evaluation for Model Interpretability—0
The Doctor Just Won't Accept That!—0
The (Un)reliability of saliency methodsCode0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
SmoothGrad: removing noise by adding noiseCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified