Optimized Pre-Processing for Discrimination Prevention

被引：0
作者：
Calmon, Flavio P. [1 ]
Wei, Dennis [2 ]
Vinzamuri, Bhanukiran [2 ]
Ramamurthy, Karthikeyan Natesan [2 ]
Varshney, Kush R. [2 ]
机构：
[1] Harvard Univ, Cambridge, MA 02138 USA
[2] IBM Res AI, Yorktown Hts, NY USA
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | 2017年 / 30卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Non-discrimination is a recognized objective in algorithmic decision making. In this paper, we introduce a novel probabilistic formulation of data pre-processing for reducing discrimination. We propose a convex optimization for learning a data transformation with three goals: controlling discrimination, limiting distortion in individual data samples, and preserving utility. We characterize the impact of limited sample size in accomplishing this objective. Two instances of the proposed optimization are applied to datasets, including one on real-world criminal recidivism. Results show that discrimination can be greatly reduced at a small cost in classification accuracy.
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页数：10
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