Causal Learning for Socially Responsible AI

被引:0
|
作者
Cheng, Lu [1 ]
Mosallanezhad, Ahmadreza [1 ]
Sheth, Paras [1 ]
Liu, Huan [1 ]
机构
[1] Arizona State Univ, Comp Sci & Engn, Tempe, AZ 85287 USA
基金
美国国家科学基金会;
关键词
INFERENCE;
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
There have been increasing concerns about Artificial Intelligence (AI) due to its unfathomable potential power. To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring fore-front the potentials and promises of CL for SRAI.
引用
收藏
页码:4374 / 4381
页数:8
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