Web: http://arxiv.org/abs/2209.10729

Sept. 23, 2022, 1:11 a.m. | Tsung-Han Wu, Shang-Tse Chen, Winston H. Hsu

cs.LG updates on arXiv.org arxiv.org

Fair Active Learning (FAL) utilized active learning techniques to achieve
high model performance with limited data and to reach fairness between
sensitive groups (e.g., genders). However, the impact of the adversarial
attack, which is vital for various safety-critical machine learning
applications, is not yet addressed in FAL. Observing this, we introduce a novel
task, Fair Robust Active Learning (FRAL), integrating conventional FAL and
adversarial robustness. FRAL requires ML models to leverage active learning
techniques to jointly achieve equalized performance on …

active learning arxiv fair

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