The skyline of counterfactual explanations for machine learning decision models

• Main Authors: Wang, Yongjie, Ding, Qinxu, Wang, Ke, Liu, Yue, Wu, Xingyu, Wang, Jinglong, Liu, Yong, Miao, Chunyan
• 其他作者: School of Computer Science and Engineering
• 格式: Conference or Workshop Item
• 語言: English
• 出版: 2022
• 主題: Engineering::Computer science and engineering; Counterfactual Explanations; Multi-Objective Optimization
• 在線閱讀: https://hdl.handle.net/10356/156946

Counterfactual explanations are minimum changes of a given input to alter the original prediction by a machine learning model, usually from an undesirable prediction to a desirable one. Previous works frame this problem as a constrained cost minimization, where the cost is defined as L1/L2 distance...