可解释性
夏普里值
计算机科学
特征(语言学)
推论
价值(数学)
国际商用机器公司
人工智能
博弈论
机器学习
理论计算机科学
数据挖掘
数理经济学
数学
语言学
哲学
材料科学
纳米技术
作者
Xinjian Luo,Yang-Fan Jiang,Xiaokui Xiao
标识
DOI:10.1145/3548606.3560573
摘要
As a solution concept in cooperative game theory, Shapley value is highly recognized in model interpretability studies and widely adopted by the leading Machine Learning as a Service (MLaaS) providers, such as Google, Microsoft, and IBM. However, as the Shapley value-based model interpretability methods have been thoroughly studied, few researchers consider the privacy risks incurred by Shapley values, despite that interpretability and privacy are two foundations of machine learning (ML) models.
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