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Common-Sense Bias Discovery and Mitigation for Classification Tasks
Machine learning model bias can arise from dataset composition: sensitive features correlated to the learning target disturb the model decision rule and lead to performance differences along the features.
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?
Provable hierarchical lifelong learning with a sketch-based modular architecture
We propose a modular architecture for lifelong learning of hierarchically structured tasks.
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