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Found 4 papers, 1 papers with code
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction.
Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution.
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers
In this paper, we aim to enable transparency in ranking interpretation by using algorithmic rankers that learn from available data and by enabling human reasoning about the learned ranking differences using explainable AI (XAI) methods.
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions.
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