Kuniko Paxton
Measuring AI Fairness in a Continuum Maintaining Nuances: A Robustness Case Study
Paxton, Kuniko; Aslansefat, Koorosh; Thakker, Dhavalkumar; Papadopoulos, Yiannis
Authors
Dr Koorosh Aslansefat K.Aslansefat@hull.ac.uk
Lecturer/Assistant Professor
Professor Dhaval Thakker D.Thakker@hull.ac.uk
Professor of Artificial Intelligence(AI) and Internet of Things(IoT)
Professor Yiannis Papadopoulos Y.I.Papadopoulos@hull.ac.uk
Professor
Abstract
As machine learning is increasingly making decisions about hiring or healthcare, we want AI to treat ethnic and socioeconomic groups fairly. Fairness is currently measured by comparing the average accuracy of reasoning across groups. We argue that improved measurement is possible on a continuum and without averaging, with the advantage that nuances could be observed within groups. Through the example of skin cancer diagnosis, we illustrate a new statistical method that works on multidimensional data and treats fairness in a continuum. We outline this new approach and focus on its robustness against three types of adversarial attacks. Indeed, such attacks can influence data in ways that may cause different levels of misdiagnosis for different skin tones, thereby distorting fairness. Our results reveal nuances that would not be evident in a strictly categorical approach.
Citation
Paxton, K., Aslansefat, K., Thakker, D., & Papadopoulos, Y. (2024). Measuring AI Fairness in a Continuum Maintaining Nuances: A Robustness Case Study. IEEE Internet Computing, 28(5), 11-19. https://doi.org/10.1109/MIC.2024.3450815
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 1, 2024 |
Online Publication Date | Aug 29, 2024 |
Publication Date | Sep 1, 2024 |
Deposit Date | Dec 10, 2024 |
Publicly Available Date | Dec 12, 2024 |
Journal | IEEE Internet Computing |
Print ISSN | 1089-7801 |
Electronic ISSN | 1941-0131 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 28 |
Issue | 5 |
Pages | 11-19 |
DOI | https://doi.org/10.1109/MIC.2024.3450815 |
Keywords | Robustness; Accuracy; Ethnicity; Machine learning; Artificial intelligence; Ethics; Social implications of technology; Sociotechnical systems; Cultural differences |
Public URL | https://hull-repository.worktribe.com/output/4834268 |
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Copyright © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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