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Found 5 papers, 2 papers with code
Evaluating Saliency Explanations in NLP by Crowdsourcing
The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications.
Estimating Treatment Effects Under Heterogeneous Interference
One popular application of this estimation lies in the prediction of the impact of a treatment (e. g., a promotion) on an outcome (e. g., sales) of a particular unit (e. g., an item), known as the individual treatment effect (ITE).
Taming Small-sample Bias in Low-budget Active Learning
Instead of tuning the coefficient for each query round, which is sensitive and time-consuming, we propose the curriculum Firth bias reduction (CHAIN) that can automatically adjust the coefficient to be adaptive to the training process.
Multiview Representation Learning from Crowdsourced Triplet Comparisons
Crowdsourcing has been used to collect data at scale in numerous fields.
Crowdsourcing Evaluation of Saliency-based XAI Methods
In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans.
