Rubric embeddings from expert criteria mitigate label bias in models trained on historical evaluations, reducing group disparities while improving cohort quality on a master's program dataset.
Counterfactual fairness
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
A generative-model-driven introspection method produces counterfactual image edits to explain deep neural network predictions on MNIST and CelebA.
citing papers explorer
-
Mitigating Label Bias with Interpretable Rubric Embeddings
Rubric embeddings from expert criteria mitigate label bias in models trained on historical evaluations, reducing group disparities while improving cohort quality on a master's program dataset.
-
Generative Counterfactual Introspection for Explainable Deep Learning
A generative-model-driven introspection method produces counterfactual image edits to explain deep neural network predictions on MNIST and CelebA.