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 311–320 of 537 papers

TitleStatusHype
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon SetCode0
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage—0
Ranking Facts for Explaining Answers to Elementary Science Questions—0
Strategizing University Rank Improvement using Interpretable Machine Learning and Data Visualization—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Shapley variable importance clouds for interpretable machine learningCode1
Multi-Agent Algorithmic Recourse—0
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Benchmark Results

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