Bias mitigation for fair automation of classification tasks

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Title: Bias mitigation for fair automation of classification tasks
Authors: Consuegra-Ayala, Juan Pablo | Gutiérrez, Yoan | Almeida-Cruz, Yudivian | Palomar, Manuel
Research Group/s: Procesamiento del Lenguaje y Sistemas de Información (GPLSI)
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Auto-ML | Bias mitigation | Ensemble methods | Fairness | Multi-objective optimization
Issue Date: 21-Oct-2024
Publisher: John Wiley & Sons
Citation: Expert Systems. 2024. https://doi.org/10.1111/exsy.13734
Abstract: The incorporation of machine learning algorithms into high-risk decision-making tasks has raised some alarms in the scientific community. Research shows that machine learning-based technologies can contain biases that cause unfair decisions for certain population groups. The fundamental danger of ignoring this problem is that machine learning methods can not only reflect the biases present in our society but could also amplify them. This article presents the design and validation of a technology to assist the fair automation of classification problems. In essence, the proposal is based on taking advantage of the intermediate solutions generated during the resolution of classification problems through using Auto-ML tools, in particular, AutoGOAL, to create unbiased/fair classifiers. The technology employs a multi-objective optimization search to find the collection of models with the best trade-offs between performance and fairness. To solve the optimization problem, we introduce a combination of Probabilistic Grammatical Evolution Search and NSGA-II. The technology was evaluated using the Adult dataset from the UCI repository, a common benchmark in related research. Results were compared with other published results in scenarios with single and multiple fairness definitions. Our experiments demonstrate the technology's ability to automate classification tasks while incorporating fairness constraints. Additionally, our method achieves competitive results against other bias mitigation techniques. A notable advantage of our approach is its minimal requirement for machine learning expertise, thanks to its Auto-ML foundation. This makes the technology accessible and valuable for advancing fairness in machine learning applications. The source code is available online for the research community.
Sponsor: This research has been partially funded by the University of Alicante and the University of Havana, the Spanish Ministry of Science and Innovation, the Generalitat Valenciana, and the European Regional Development Fund (ERDF) through the following funding: At the national level, the following projects were granted: TRIVIAL (PID2021-122263OB-C22); CORTEX (PID2021-123956OB-I00); CLEARTEXT (TED2021-130707B-I00); and SOCIALTRUST (PDC2022-133146-C22), funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by ERDF A way of making Europe, by the European Union or by the European Union NextGenerationEU/PRTR. Also, the VIVES: ‘Pla de Tecnologies de la Llengua per al valencià’ project (2022/TL22/00215334) from the Projecte Estratègic per a la Recuperació i Transformació Econòmica (PERTE). At regional level, the Generalitat Valenciana (Conselleria d'Educacio, Investigacio, Cultura i Esport), granted funding for NL4DISMIS (CIPROM/2021/21). Moreover, it was backed by the work of two COST Actions: CA19134—‘Distributed Knowledge Graphs’ and CA19142—‘Leading Platform for European Citizens, Industries, Academia, and Policymakers in Media Accessibility’.
URI: http://hdl.handle.net/10045/148340
ISSN: 0266-4720 (Print) | 1468-0394 (Online)
DOI: 10.1111/exsy.13734
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2024 John Wiley & Sons Ltd.
Peer Review: si
Publisher version: https://doi.org/10.1111/exsy.13734
Appears in Collections:INV - GPLSI - Artículos de Revistas

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