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Found 3 papers, 2 papers with code
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Recently, interpretable machine learning has re-explored concept bottleneck models (CBM), comprising step-by-step prediction of the high-level concepts from the raw features and the target variable from the predicted concepts.
This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Prototype learning, a popular machine learning method designed for inherently interpretable decisions, leverages similarities to learned prototypes for classifying new data.
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss.
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