Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making

Sina Aghaei · Mohammad Javad Azizi · Phebe Vayanos

135 citations0/0 files storedProceedings of the AAAI Conference on Artificial Intelligence

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Ethics and Social Impacts of AI Data Mining Algorithms and Applications Explainable Artificial Intelligence (XAI)

Abstract

In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decisionmaking (e.g., to decide who to admit into a degree program or to prioritize individuals for public housing). Yet, these automated tools may result in discriminative decision-making in the sense that they may treat individuals unfairly or unequally based on membership to a category or a minority, resulting in disparate treatment or disparate impact and violating both moral and ethical standards. This may happen when the training dataset is itself biased (e.g., if individuals belonging to a particular group have historically been discriminated upon). However, it may also happen when the training dataset is unbiased, if the errors made by the system affect individuals belonging to a category or minority differently (e.g., if misclassification rates for Blacks are higher than for Whites). In this paper, we unify the definitions of unfairness across classification and regression. We propose a versatile mixed-integer optimization framework for learning optimal and fair decision trees and variants thereof to prevent disparate treatment and/or disparate impact as appropriate. This translates to a flexible schema for designing fair and interpretable policies suitable for socially sensitive decision-making. We conduct extensive computational studies that show that our framework improves the state-of-the-art in the field (which typically relies on heuristics) to yield non-discriminative decisions at lower cost to overall accuracy.

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References

2012Fairness through awareness 2014Big Data's Disparate Impact 2016Equality of Opportunity in Supervised Learning 2011Data preprocessing techniques for classification without discrimination 2017Algorithmic Decision Making and the Cost of Fairness 2013Learning Fair Representations 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients 2010Three naive Bayes approaches for discrimination-free classification

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2022In-Processing Modeling Techniques for Machine Learning Fairness: A Survey 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifier 2020Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination 2024Addressing bias in bagging and boosting regression models 2023Market Segmentation Trees 2020Robust Optimization for Fairness with Noisy Protected Groups

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