.png){kind=icon}
返回
Fair and Private Data Preprocessing through Microaggregation
.png){kind=icon}
OA
AI
DOI：10.1145/3617377.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Privacy protection for personal data and fairness in automated decisions are fundamental requirements for responsibleMachine Learning. Both may be enforced through data preprocessing and share a common target: data should remain useful for a task, while becoming uninformative of the sensitive information. The intrinsic connection between privacy and fairness implies that modifications performed to guarantee one of these goals, may have an effect on the other, e.g., hiding a sensitive attribute from a classification algorithm might prevent a biased decision rule having such attribute as a criterion. This work resides at the intersection of algorithmic fairness and privacy. We show how the two goals are compatible, and may be simultaneously achieved, with a small loss in predictive performance. Our results are competitive with both state-of-the-art fairness correcting algorithms and hybrid privacy-fairness methods. Experiments were performed on three widely used benchmark datasets: Adult Income, COMPAS, and German Credit.
Keyword:
Responsible machine learning
fair classification
privacy preserving data mining
algorithmic fairness
ethical AI
期刊
IF:
4.8
论文数:
1.3K
被引数:
4.4K
机构
引用论文
分享收藏Tachykinin and Calcitonin Gene-Related Peptide Immunoreactivities and Mrnas in the Mammalian Enteric Nervous System and Sensory Ganglia哺乳动物肠神经系统和感觉神经节中的速激肽和降钙素基因相关肽的免疫反应性和mrna
PREAI
分享收藏Neuronal-binding antibodies from patients with antiphospholipid syndrome induce cognitive deficits following intrathecal passive transfer.png)
Lupus
LupusIF0
PREAI
分享收藏分享收藏Trend and conservation of saker falcon (Falco cherrug) population in western Slovakia between 1976 and 2010西斯洛伐克地区1976年至2010年间猎隼（Falco cherrug）种群的动态趋势与保护情况
OAAI
OAAI分享收藏Preparation and Characterization of Inclusion Complexes of Poly(propylene glycol) with Cyclodextrins
PREAI
分享收藏
查看更多内容