LUCID-GAN：用于定位不公平性的条件生成模型
LUCID-GAN: Conditional Generative Models to Locate Unfairness
AI总结：
提出LUCID-GAN，利用条件生成模型替代梯度逆向设计生成规范输入，以检测黑盒模型中的不道德偏见，克服了传统统计指标缺乏可解释性及不可微模型适用性等局限。
摘要：
大多数群体公平性概念通过计算模型输出的统计奇偶性指标来检测不道德的偏见。然而，这种方法存在几个缺点，如哲学上的分歧、相互不兼容以及缺乏可解释性。这些缺点推动了互补偏差检测方法的研究，这些方法为歧视的来源提供了额外的透明度，并且对关于公平性定义和受保护特征选择的先验决策是不可知的。这一方向的一个近期提议是LUCID（通过规范逆向设计定位不公平性），其中规范集是通过对输入空间执行梯度下降生成的，从而在给定首选输出的情况下揭示模型所需的输入。关于模型机制的这些信息，即哪些特征值对于获得特定输出是必不可少的，能够暴露其内部逻辑中潜在的不道德偏见。在这里，我们提出了LUCID-GAN，它通过条件生成模型而不是基于梯度的逆向设计来生成规范输入。LUCID-GAN具有几个优势，包括它适用于不可微模型，确保规范集由现实的输入组成，并允许评估代理和交叉歧视。我们在UCI Adult和COMPAS数据集上对LUCID-GAN进行了实证评估，并表明它允许在无需访问训练数据的情况下检测黑盒模型中的不道德偏见。
英文摘要：
Most group fairness notions detect unethical biases by computing statistical parity metrics on a model's output. However, this approach suffers from several shortcomings, such as philosophical disagreement, mutual incompatibility, and lack of interpretability. These shortcomings have spurred the research on complementary bias detection methods that offer additional transparency into the sources of discrimination and are agnostic towards an a priori decision on the definition of fairness and choice of protected features. A recent proposal in this direction is LUCID (Locating Unfairness through Canonical Inverse Design), where canonical sets are generated by performing gradient descent on the input space, revealing a model's desired input given a preferred output. This information about the model's mechanisms, i.e., which feature values are essential to obtain specific outputs, allows exposing potential unethical biases in its internal logic. Here, we present LUCID-GAN, which generates canonical inputs via a conditional generative model instead of gradient-based inverse design. LUCID-GAN has several benefits, including that it applies to non-differentiable models, ensures that canonical sets consist of realistic inputs, and allows to assess proxy and intersectional discrimination. We empirically evaluate LUCID-GAN on the UCI Adult and COMPAS data sets and show that it allows for detecting unethical biases in black-box models without requiring access to the training data.
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。
