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We study the problem of formally verifying individual fairness of decision tree ensembles, as well as training tree models which maximize both accuracy and individual fairness.
Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints
Cousot, P.; and Cousot, R. 1977 · 1977
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Classification and Regression Trees
Breiman, L.; Friedman, J. H.; Olshen, R. A.; and Stone, C. J. 1984 · 1984
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Genetic algorithms and adaptation
Holland, J. H. 1984 · 1984
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Genetic algorithms: a survey
Srinivas, M.; and Patnaik, L. M. 1994 · 1994
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A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
Kearns, M. J.; and Mansour, Y. 1998 · 1998
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Random Forests
Breiman, L. 2001 · 2001
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Greedy Function Approximation: A Gradient Boosting Machine
Friedman, J. H. 2001 · 2001
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Avoiding overfitting of decision trees
Bramer, M. 2007 · 2007
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Consumer Credit-Risk Models via Machine-Learning Algorithms
Khandani, A. E.; Kim, A. J.; and Lo, A. W. 2010 · 2010
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Fairness Through Awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Genetic Adversarial Training of Decision Trees
Ranzato, F.; and Zanella, M. 2020b · 2012
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Machine Bias
Angwin, J.; Larson, J.; Mattu, S.; and Kirchner, L. 2016 · 2016
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Big Data’s Disparate Impact
Barocas, S.; and Selbst, A. D. 2016 · 2016
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Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Chouldechova, A. 2017 · 2016
Cited alongside, same era.
Equality of Opportunity in Supervised Learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Evasion and Hardening of Tree Ensemble Classifiers
Kantchelian, A.; Tygar, J. D.; and Joseph, A. D. 2016 · 2016
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Optimal classification trees
Bertsimas, D.; and Dunn, J. 2017 · 2017
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Towards Evaluating the Robustness of Neural Networks
Carlini, N.; and Wagner, D. A. 2017 · 2017
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Adversarial Training of Gradient-Boosted Decision Trees
Calzavara, S.; Lucchese, C.; and Tolomei, G. 2019 · 2019
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Robust Decision Trees Against Adversarial Examples
Chen, H.; Zhang, H.; Boning, D. S.; and Hsieh, C. 2019 · 2019
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TREANT: training evasion-aware decision trees
Calzavara, S.; Lucchese, C.; Tolomei, G.; Abebe, S. A.; and Orlando, S. 2020 · 2020
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Achieving Fairness with Decision Trees: An Adversarial Approach
Grari, V.; Ruf, B.; Lamprier, S.; and Detyniecki, M. 2020 · 2020
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Abstract Interpretation of Decision Tree Ensemble Classifiers
Ranzato, F.; and Zanella, M. 2020a · 2020
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Introduction to Static Analysis: An Abstract Interpretation Perspective
Rival, X.; and Yi, K. 2020 · 2020
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Dua, D.; and Graff, C. 2017 · 2017
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Zafar, M. B.; Valera, I.; Gomez-Rodriguez, M.; and Gummadi, K. P. 2017 · 2017
Cited alongside, same era.
Making Machine Learning Robust Against Adversarial Inputs
Goodfellow, I.; McDaniel, P.; and Papernot, N. 2018 · 2018
Cited alongside, same era.
Fair Forests: Regularized Tree Induction to Minimize Model Bias
Raff, E.; Sylvester, J.; and Mills, S. 2018 · 2018
Cited alongside, same era.
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
Aghaei, S.; Azizi, M. J.; and Vayanos, P. 2019 · 2019
Cited alongside, same era.
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Andriushchenko, M.; and Hein, M. 2019 · 2019
Cited alongside, same era.
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Roh, Y.; Lee, K.; Whang, S.; and Suh, C. 2020 · 2020
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Learning Certified Individually Fair Representations
Ruoss, A.; Balunovic, M.; Fischer, M.; and Vechev, M. 2020 · 2020
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We Need Fairness and Explainability in Algorithmic Hiring
Schumann, C.; Foster, J. S.; Mattei, N.; and Dickerson, J. P. 2020 · 2020
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Perfectly Parallel Fairness Certification of Neural Networks
Urban, C.; Christakis, M.; Wüstholz, V.; and Zhang, F. 2020 · 2020
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Training individually fair ML models with sensitive subspace robustness
Yurochkin, M.; Bower, A.; and Sun, Y. 2020 · 2020
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