7 citations · 17 across the 8 of their papers we have counts for
4 papers · 1 filter
Consistent Range Approximation for Fair Predictive Modeling
Jiongli Zhu, Sainyam Galhotra, Nazanin Sabri +1
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databas…
On Explaining Confounding Bias
Brit Youngmann, Michael Cafarella, Yuval Moskovitch +1
When analyzing large datasets, analysts are often interested in the explanations for surprising or unexpected results produced by their queries. In this work, we focus on aggregate…
Combining Counterfactuals With Shapley Values To Explain Image Models
Aditya Lahiri, Kamran Alipour, Ehsan Adeli +1
With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabu…
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces
Kamran Alipour, Aditya Lahiri, Ehsan Adeli +2
Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfact…