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Learning Fair Rule Lists
TL;DR: The empirical evaluation of FairCORELS on real-world datasets demonstrates that it outperforms state-of-the-art fair classification techniques that are interpretable by design while being competitive with non-interpretable ones.
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Abstract: As the use of black-box models becomes ubiquitous in high stake decision-making systems, demands for fair and interpretable models are increasing. While it has been shown that interpretable models can be as accurate as black-box models in several critical domains, existing fair classification techniques that are interpretable by design often display poor accuracy/fairness tradeoffs in comparison with their non-interpretable counterparts. In this paper, we propose FairCORELS, a fair classification technique interpretable by design, whose objective is to learn fair rule lists. Our solution is a multi-objective variant of CORELS, a branch-and-bound algorithm to learn rule lists, that supports several statistical notions of fairness. Examples of such measures include statistical parity, equal opportunity and equalized odds. The empirical evaluation of FairCORELS on real-world datasets demonstrates that it outperforms state-of-the-art fair classification techniques that are interpretable by design while being competitive with non-interpretable ones.
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Citations
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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
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Jimmy Lin,Chudi Zhong,Diane Hu,Cynthia Rudin,Margo Seltzer +4 more
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Generalized and Scalable Optimal Sparse Decision Trees
TL;DR: In this article, the authors provide a general framework for decision tree optimization that addresses the two significant open problems in the area: treatment of imbalanced data and fully optimizing over continuous variables, and present techniques that produce optimal decision trees over a variety of objectives including F-score, AUC, and partial area under the ROC convex hull.
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Generalized Optimal Sparse Decision Trees.
TL;DR: The contribution in this work is to provide a general framework for decision tree optimization that addresses the two significant open problems in the area: treatment of imbalanced data and fully optimizing over continuous variables.
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FairCORELS, an Open-Source Library for Learning Fair Rule Lists
Ulrich Aïvodji,Julien Ferry,Sébastien Gambs,Marie-José Huguet,Mohamed Siala +4 more
- 26 Oct 2021
TL;DR: FairCORELS as mentioned in this paper is an open-source Python module for building fair rule lists, which supports six statistical fairness metrics, proposes several exploration parameters and leverages on the fairness constraints to prune the search space efficiently.
6
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
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TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan,Andrea Vedaldi,Andrew Zisserman +2 more
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TL;DR: In this paper, the gradient of the class score with respect to the input image is computed to compute a class saliency map, which can be used for weakly supervised object segmentation using classification ConvNets.
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