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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 461–470 of 537 papers

TitleStatusHype
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Explainable AI using expressive Boolean formulas—0
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Benchmark Results

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