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arXiv 2204.06917cs.LGcs.AIcs.CYstat.ML

Global Counterfactual Explanations: Investigations, Implementations and Improvements

J.P. Morgan AI Research(摩根大通人工智能研究院)

Dan Ley, Saumitra Mishra, Daniele Magazzeni

英文摘要:

Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While some works touch upon the notion of a global explanation, typically suggesting to aggregate masses of local explanations in the hope of ascertaining global properties, few provide frameworks that are either reliable or computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to investigate existing global methods, with a focus on implementing and improving Actionable Recourse Summaries (AReS), the only known global counterfactual explanation framework for recourse.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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