Automated Fairness Testing with Representative Sampling

Umutcan Karakas, Ayse Tosun

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Özet

The issue of fairness testing in machine learning models has become popular due to rising concerns about potential bias and discrimination, as these models continue to permeate end-user applications. However, achieving an accurate and reliable measurement of the fairness performance of machine learning models remains a substantial challenge. Representative sampling plays a pivotal role in ensuring accurate fairness assessments and providing insight into the underlying dynamics of data, unlike biased or random sampling approaches. In our study, we introduce our approach, namely RSFair, which adopts the representative sampling method to comprehensively evaluate the fairness performance of a trained machine learning model. Our research findings on two datasets indicate that RSFair yields more accurate and reliable results, thus improving the efficiency of subsequent search steps, and ultimately the fairness performance of the model. With the usage of Orthogonal Matching Pursuit (OMP) and K-Singular Value Decomposition (K-SVD) algorithms for representative sampling, RSFair significantly improves the detection of discriminatory inputs by 76% and the fairness performance by 53% compared to other search-based approaches in the literature.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıPROMISE 2023 - Proceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering, Co-located with
Ana bilgisayar yayını alt yazısıESEC/FSE 2023
YayınlayanAssociation for Computing Machinery
Sayfalar54-63
Sayfa sayısı10
ISBN (Elektronik)9798400703751
DOI'lar
Yayın durumuYayınlandı - 8 Ara 2023
Etkinlik19th International Conference on Predictive Models and Data Analytics in Software Engineering, Co-located with: ESEC/FSE 2023 - San Francisco, United States
Süre: 8 Ara 2023 → …

Yayın serisi

AdıACM International Conference Proceeding Series

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???event.eventtypes.event.conference???19th International Conference on Predictive Models and Data Analytics in Software Engineering, Co-located with: ESEC/FSE 2023
Ülke/BölgeUnited States
ŞehirSan Francisco
Periyot8/12/23 → …

Bibliyografik not

Publisher Copyright:
© 2023 Owner/Author.

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