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FairEd: A Systematic Fairness Analysis Approach Applied in a Higher Educational Context

Authors:

Jonathan Vasquez Verdugo,

Xavier Gitiaux,

Huzefa RangwalaAuthors Info & Claims

LAK22: LAK22: 12th International Learning Analytics and Knowledge Conference

Pages 271 - 281

https://doi.org/10.1145/3506860.3506902

Published: 21 March 2022 Publication History

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Abstract

Higher education institutions increasingly rely on machine learning models. However, a growing body of evidence shows that these algorithms may not serve underprivileged communities well and at times discriminate against them. This is all the more concerning in education as negative outcomes have long-term implications. We propose a systematic process for framing, detecting, documenting, and reporting unfairness risks. The systematic approach’s outcomes are merged into a framework named FairEd, which would help decision-makers to understand unfairness risks along the environmental and analytical fairness dimension. The tool allows to decide (i) whether the dataset contains risks of unfairness; (ii) how the models could perform along many fairness dimensions; (iii) whether potentially unfair outcomes can be mitigated without degrading performance. The systematic approach is applied to a Chilean University case study, where a predicting student dropout model is aimed to build. First, we capture the nuances of the Chilean context where unfairness emerges along income lines and demographic groups. Second, we highlight the benefit of reporting unfairness risks along a diverse set of metrics to shed light on potential discrimination. Third, we find that measuring the cost of fairness is an important quantity to report on when doing the model selection.

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Cited By

Pham NPham Ngoc HNguyen-Duc A(2025)Fairness for machine learning software in educationJournal of Systems and Software10.1016/j.jss.2024.112244219:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.jss.2024.112244
Colpo MThompsen Primo TAguiar MCechinel C(2024)Educational Data Mining for Dropout Prediction: Trends, Opportunities, and ChallengesRevista Brasileira de Informática na Educação10.5753/rbie.2024.355932(220-256)Online publication date: 20-May-2024
https://doi.org/10.5753/rbie.2024.3559
Xu ZOlson JPochinki NZheng ZYu R(2024)Contexts Matter but How? Course-Level Correlates of Performance and Fairness Shift in Predictive Model TransferProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636936(713-724)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636936
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cover image ACM Other conferences

LAK22: LAK22: 12th International Learning Analytics and Knowledge Conference

March 2022

582 pages

ISBN:9781450395731

DOI:10.1145/3506860

Program Chairs:
Alyssa Friend Wise
New York University, USA
,
Roberto Martinez-Maldonado
Monash University, Australia
,
Isabel Hilliger
Pontificia Universidad Católica de Chile, Chile

Copyright © 2022 ACM.

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Published: 21 March 2022

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LAK22: 12th International Learning Analytics and Knowledge Conference

March 21 - 25, 2022

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Cited By

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https://dl.acm.org/doi/10.1016/j.jss.2024.112244
Colpo MThompsen Primo TAguiar MCechinel C(2024)Educational Data Mining for Dropout Prediction: Trends, Opportunities, and ChallengesRevista Brasileira de Informática na Educação10.5753/rbie.2024.355932(220-256)Online publication date: 20-May-2024
https://doi.org/10.5753/rbie.2024.3559
Xu ZOlson JPochinki NZheng ZYu R(2024)Contexts Matter but How? Course-Level Correlates of Performance and Fairness Shift in Predictive Model TransferProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636936(713-724)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636936
Zambrano AZhang JBaker R(2024)Investigating Algorithmic Bias on Bayesian Knowledge Tracing and Carelessness DetectorsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636890(349-359)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636890
Cohausz LKappenberger JStuckenschmidt H(2024)What Fairness Metrics Can Really Tell You: A Case Study in the Educational DomainProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636873(792-799)Online publication date: 18-Mar-2024
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Mangal MPardos Z(2024) Implementing equitable and intersectionality‐aware ML in education: A practical guide British Journal of Educational Technology10.1111/bjet.1348455:5(2003-2038)Online publication date: 23-May-2024
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Micheli MHupont IDelipetrev BSoler-Garrido J(2023)The landscape of data and AI documentation approaches in the European policy contextEthics and Information Technology10.1007/s10676-023-09725-725:4Online publication date: 28-Oct-2023
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Le Quy TFriege GNtoutsi E(2023)A Review of Clustering Models in Educational Data Science Toward Fairness-Aware LearningEducational Data Science: Essentials, Approaches, and Tendencies10.1007/978-981-99-0026-8_2(43-94)Online publication date: 30-Apr-2023
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