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 331–340 of 537 papers

TitleStatusHype
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Machine Learning for Economic Forecasting: An Application to China's GDP Growth—0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
Additive Higher-Order Factorization Machines—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Tensor Polynomial Additive Model—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
MAntRA: A framework for model agnostic reliability analysis—0
The Doctor Just Won't Accept That!—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
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Benchmark Results

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