论文标题
潜在-CF：反向反事实解释的简单基线
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
论文作者
论文摘要
在公平贷款法律和一般数据保护法规（GDPR）的环境中，解释模型预测的能力至关重要。高质量的解释是评估公平性的第一步。反事实是可解释性的宝贵工具。他们为受到预测决定的个人提供了可行的，可理解的解释。找到生产它们的基线很重要。我们提出了一种通过使用梯度下降来在自动编码器的潜在空间中搜索我们的方法来生成反事实的简单方法，并基于我们的方法来反对在特征空间中搜索反事实的方法。此外，我们实施指标来具体评估反事实的质量。我们表明，潜在空间反事实生成在基本特征梯度下降方法的速度与更复杂的面向特征空间的技术产生的反事实的稀疏性和真实性之间达到了平衡。
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance. High quality explanations are the first step in assessing fairness. Counterfactuals are valuable tools for explainability. They provide actionable, comprehensible explanations for the individual who is subject to decisions made from the prediction. It is important to find a baseline for producing them. We propose a simple method for generating counterfactuals by using gradient descent to search in the latent space of an autoencoder and benchmark our method against approaches that search for counterfactuals in feature space. Additionally, we implement metrics to concretely evaluate the quality of the counterfactuals. We show that latent space counterfactual generation strikes a balance between the speed of basic feature gradient descent methods and the sparseness and authenticity of counterfactuals generated by more complex feature space oriented techniques.
