Bagriacik, Meryem, Otero, Fernando E.B. (2024) Multiple fairness criteria in decision tree learning. Applied Soft Computing, 167 . Article Number 112313. ISSN 1568-4946. E-ISSN 1872-9681. (doi:10.1016/j.asoc.2024.112313) (KAR id:107704)
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Official URL: https://doi.org/10.1016/j.asoc.2024.112313 |
Abstract
The use of algorithmic decision-making systems based on machine learning models has led to a need for fair (unbiased) and explainable classification outcomes. In particular, machine learning algorithms can encode biases, which might result in discriminatory decisions for certain groups such as gender, race, or age. Although a number of works on decision tree learning have been proposed to decrease the chance of discrimination, they usually focus on the use of a single fairness metric. In general, creating a model based on a single fairness metric is not a sufficient way to mitigate discrimination since bias can originate from various sources—e.g., the data itself or the optimization process. In this paper, we propose a novel decision tree learning process that utilizes multiple fairness metrics to address both group and individual discrimination. This is achieved by extending the attribute selection procedure to consider not only information gain but also gain in fairness. Computational experiments on fourteen different datasets with various sensitive features demonstrate that the proposed Fair-C4.5 models improve fairness without a loss in predictive accuracy when compared to the well-known C4.5 and the fairness-aware FFTree algorithms.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.asoc.2024.112313 |
Uncontrolled keywords: | fairness; decision tree; classification; interpretability |
Subjects: | Q Science > Q Science (General) > Q335 Artificial intelligence |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Fernando Otero |
Date Deposited: | 04 Nov 2024 15:59 UTC |
Last Modified: | 05 Nov 2024 13:13 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107704 (The current URI for this page, for reference purposes) |
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