WebCounterfactual explana-tions state what would have happened had the input variables been changed in certain ways. These explanations are particularly useful when you want to help the user understand how they can change ... Robustness as a general desideratum has long been advocated by both natural and social scientists [6, 19]. The idea of ... WebJan 1, 2024 · Download Citation Counterfactual Sensitivity and Robustness We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent ...
Can We Improve Model Robustness through Secondary …
Webthat the robust classifier is less sensitive to spurious corre-lations. While these prior works show the potential of using human annotations to improve model robustness, collecting such annotations can be costly. In this paper, we propose to train a robust classifier with automatically generated counterfactual samples. Specifi- WebSep 27, 2024 · In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different? Toxicity classifiers demonstrate a counterfactual fairness issue by predicting that "Some people are gay" is toxic while "Some people are … troy bilt lawn mower trackid sp-006
智能论文笔记
WebSep 21, 2024 · Counterfactual Sensitivity and Robustness Timothy Christensen and Benjamin Connault Northwestern Interactions Workshop. Discussion comments by Doug Miller. September 21, 2024 ... no need to specify a narrow family to be robust within • Stay within a neighborhood (delta) of baseline • Conceptual approach: search over all possible ... WebCounterfactual Sensitivity and Robustness∗ Timothy Christensen† Benjamin Connault‡ May 25, 2024 Abstract We propose a framework for analyzing the sensitivity of counterfactuals to para-metric assumptions about the distribution of latent variables in structural models. WebJan 1, 2024 · COUNTERFACTUAL SENSITIVITY AND ROBUSTNESS 3 identical across all ∈ . It therefore suffices to form the criterion using only the d averaged moments g rather than the full set of ×dmoments, thereby reducing the dimension of the inner optimization by a factor of . REMARK A.2: When Monte Carlo integration is used to compute … troy bilt lawn mower wikipedia