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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 526–537 of 537 papers

Title	Date	Tasks	Status
Socio-economic disparities and COVID-19 in the USA	Sep 10, 2020	Interpretable Machine Learning	CodeCode Available
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework	Oct 21, 2019	Interpretable Machine Learning	CodeCode Available
Comparative Document Summarisation via Classification	Dec 6, 2018	Binary ClassificationClassification	CodeCode Available
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry	May 12, 2025	Dimensionality ReductionInterpretable Machine Learning	CodeCode Available
Manipulating and Measuring Model Interpretability	Feb 21, 2018	BIG-bench Machine LearningDecision Making	CodeCode Available
An exact counterfactual-example-based approach to tree-ensemble models interpretability	May 31, 2021	counterfactualCounterfactual Explanation	CodeCode Available
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning	Nov 9, 2022	Deep LearningDiagnostic	CodeCode Available
Margin Optimal Classification Trees	Oct 19, 2022	Binary ClassificationClassification	CodeCode Available
Understanding Interventional TreeSHAP : How and Why it Works	Sep 29, 2022	Interpretable Machine Learning	CodeCode Available
An Additive Instance-Wise Approach to Multi-class Model Interpretation	Jul 7, 2022	Additive modelsInterpretable Machine Learning	CodeCode Available
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition	Jun 10, 2025	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available
Probing hidden spin order with interpretable machine learning	Apr 23, 2018	BIG-bench Machine LearningInterpretable Machine Learning	CodeCode Available

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Q-SENN	Top 1 Accuracy	85.9	—	Unverified
2	SLDD-Model	Top 1 Accuracy	85.7	—	Unverified