Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning

被引:0
作者
Byrne, Ruth M. J. [1 ,2 ]
机构
[1] Univ Dublin, Trinity Coll Dublin, Sch Psychol, Dublin, Ireland
[2] Univ Dublin, Trinity Coll Dublin, Inst Neurosci, Dublin, Ireland
来源
PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2019年
关键词
PSYCHOLOGICAL DISTANCE; TEMPORAL-ORDER; THINKING; THOUGHTS; SIMULATION;
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
Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI). Counterfactuals can aid the provision of interpretable models to make the decisions of inscrutable systems intelligible to developers and users. However, not all counterfactuals are equally helpful in assisting human comprehension. Discoveries about the nature of the counterfactuals that humans create are a helpful guide to maximize the effectiveness of counterfactual use in AI.
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页码:6276 / 6282
页数:7
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