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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 371–380 of 537 papers

TitleStatusHype
Levels of explainable artificial intelligence for human-aligned conversational explanations—0
Learning Gradual Argumentation Frameworks using Genetic AlgorithmsCode0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Developing a Fidelity Evaluation Approach for Interpretable Machine LearningCode0
Counterfactual Explanations for Survival Prediction of Cardiovascular ICU PatientsCode0
Full interpretable machine learning in 2D with inline coordinates—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray ImagesCode0
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival dataCode0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
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Benchmark Results

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