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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

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

February 1, 2023

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Authors

Sina Aghaei

University of Southern California


Mohammad Javad Azizi

University of Southern California


Phebe Vayanos

University of Southern California


DOI:

10.1609/aaai.v33i01.33011418


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.

Topics: AAAI

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HOW TO CITE:

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making Proceedings of the AAAI Conference on Artificial Intelligence (2019) 1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making AAAI 2019, 1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos (2019). Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making. Proceedings of the AAAI Conference on Artificial Intelligence, 1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos. Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos. 2019. Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making. "Proceedings of the AAAI Conference on Artificial Intelligence". 1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos. (2019) "Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making", Proceedings of the AAAI Conference on Artificial Intelligence, p.1418-1426

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos, "Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making", AAAI, p.1418-1426, 2019.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos. "Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos. "Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 1418-1426.

Sina Aghaei||Mohammad Javad Azizi||Phebe Vayanos. Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making. AAAI[Internet]. 2019[cited 2023]; 1418-1426.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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