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Found 5 papers, 3 papers with code
Model Agnostic Explainable Selective Regression via Uncertainty Estimation
Such a framework is known as selective prediction.
ScandEval: A Benchmark for Scandinavian Natural Language Processing
This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages.
Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda
We conclude by offering a tentative path toward reflexive and responsible development of ML tools for moderating misinformation and other harmful content online.
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Training these machine learning models require datasets of sufficient scale, diversity and quality.
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on MuMiN-small
Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available.
