Fairness-guided SMT-based rectification of decision trees and random forests
J Zhang, I Beschastnikh, S Mechtaev… - arXiv preprint arXiv …, 2020 - arxiv.org
Data-driven decision making is gaining prominence with the popularity of various machine
learning models. Unfortunately, real-life data used in machine learning training may capture
human biases, and as a result the learned models may lead to unfair decision making. In
this paper, we provide a solution to this problem for decision trees and random forests. Our
approach converts any decision tree or random forest into a fair one with respect to a
specific data set, fairness criteria, and sensitive attributes. The\emph {FairRepair} tool, built …
learning models. Unfortunately, real-life data used in machine learning training may capture
human biases, and as a result the learned models may lead to unfair decision making. In
this paper, we provide a solution to this problem for decision trees and random forests. Our
approach converts any decision tree or random forest into a fair one with respect to a
specific data set, fairness criteria, and sensitive attributes. The\emph {FairRepair} tool, built …
[CITATION][C] Fairness-guided SMT-based Rectification of Decision Trees and Random Forests. CoRR abs/2011.11001 (2020)
J Zhang, I Beschastnikh, S Mechtaev, A Roychoudhury - arXiv preprint arXiv …, 2020
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