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Found 5 papers, 1 papers with code
Verified Training for Counterfactual Explanation Robustness under Data Shift
Our empirical evaluation demonstrates that VeriTraCER generates CEs that (1) are verifiably robust to small model updates and (2) display competitive robustness to state-of-the-art approaches in handling empirical model updates including random initialization, leave-one-out, and distribution shifts.
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
To this end, we conduct rigorous theoretical analysis to demonstrate that model curvature, weight decay parameters while training, and the magnitude of the dataset shift are key factors that determine the extent of explanation (in)stability.
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions
We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social bias in training datasets impact test-time predictions.
Certifying Data-Bias Robustness in Linear Regression
Datasets typically contain inaccuracies due to human error and societal biases, and these inaccuracies can affect the outcomes of models trained on such datasets.
Certifying Robustness to Programmable Data Bias in Decision Trees
To certify robustness, we use a novel symbolic technique to evaluate a decision-tree learner on a large, or infinite, number of datasets, certifying that each and every dataset produces the same prediction for a specific test point.
