Machine learningExplainable AI
反事实解释
反事实解释由 Wachter、Mittelstadt 和 Russell 于 2017 年提出，旨在回答以下问题：“输入需要发生多小的变化才能产生不同的模型输出？” 它不解释模型为何做出某个决策，而是描述需要改变什么才能使该决策被逆转，这使得它在高风险应用（如信用评分、医疗诊断和招聘决策）中，尤其是在欧盟 GDPR 等框架下，具有极高的价值。
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来源
- Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31, 841–887. link ↗
如何引用本页
ScholarGate. (2026, June 2). Counterfactual Explanations. ScholarGate. https://scholargate.app/zh/machine-learning/counterfactual-explanations
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