research-article

Towards model-based bias mitigation in machine learning

Authors:

Dimitris KolovosAuthors Info & Claims

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

Pages 143 - 153

https://doi.org/10.1145/3550355.3552401

Published: 24 October 2022 Publication History

Abstract

Models produced by machine learning are not guaranteed to be free from bias, particularly when trained and tested with data produced in discriminatory environments. The bias can be unethical, mainly when the data contains sensitive attributes, such as sex, race, age, etc. Some approaches have contributed to mitigating such biases by providing bias metrics and mitigation algorithms. The challenge is users have to implement their code in general/statistical programming languages, which can be demanding for users with little programming and fairness in machine learning experience. We present FairML, a model-based approach to facilitate bias measurement and mitigation with reduced software development effort. Our evaluation shows that FairML requires fewer lines of code to produce comparable measurement values to the ones produced by the baseline code.

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Towards model-based bias mitigation in machine learning

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cover image ACM Conferences

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

October 2022

412 pages

ISBN:9781450394666

DOI:10.1145/3550355

General Chairs:
Eugene Syriani
Université de Montréal, Canada
,
Houari Sahraoui
Université de Montréal, Canada

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Published: 24 October 2022

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MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems

October 23 - 28, 2022

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Naveed HGrundy JArora CKhalajzadeh HHaggag OEgyed AWimmer MChechik MCombemale B(2024)Towards Runtime Monitoring for Responsible Machine Learning using Model-driven EngineeringProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3640310.3674092(195-202)Online publication date: 22-Sep-2024
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Naveed HArora CKhalajzadeh HGrundy JHaggag O(2024)Model driven engineering for machine learning componentsInformation and Software Technology10.1016/j.infsof.2024.107423169:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.infsof.2024.107423
Naveed H(2023)Runtime Monitoring of Human-Centric Requirements in Machine Learning Components: A Model-Driven Engineering Approach2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C59198.2023.00040(146-152)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS-C59198.2023.00040
Morales SClarisó RCabot JBissyandé TKlein JBird CSarro F(2023)Automating Bias Testing of LLMsProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00018(1705-1707)Online publication date: 11-Nov-2023
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