一句话：提出信息论测试与调试框架 Dice，量化并按严重性排序 DNN 公平性缺陷，因果定位问题层与神经元。
深度前馈神经网络用于关键决策支持时，可能放大训练数据偏见而产生公平性缺陷。现有搜索测试能发现缺陷，却缺少严重性和因果解释等调试辅助。本文提出 Dice：用信息论将公平性量化为决策中使用的受保护信息位数，据此排序缺陷并平滑搜索空间；再通过因果调试定位导致缺陷的未充分训练层与神经元。在十个社会关键任务的 DNN 上，Dice 有效刻画歧视程度，较先进技术更高效生成歧视性实例，并定位显著偏见的层/神经元。
The deep feedforward neural networks (DNNs) are increasingly deployed in socioeconomic critical decision support software systems. DNNs are exceptionally good at finding min-imal, sufficient statistical patterns within their training data. Consequently, DNNs may learn to encode decisions-amplifying existing biases or introducing new ones-that may disadvantage protected individuals/groups and may stand to violate legal protections. While the existing search based software testing approaches have been effective in discovering fairness defects, they do not supplement these defects with debugging aids-such as severity and causal explanations-crucial to help developers triage and decide on the next course of action. Can we measure the severity of fairness defects in DNNs? Are these defects symptomatic of improper training or they merely reflect biases present in the training data? To answer such questions, we present Dice: an information-theoretic testing and debugging framework to discover and localize fairness defects in DNNs. The key goal of Dice is to assist software developers in triaging fairness defects by ordering them by their severity. Towards this goal, we quantify fairness in terms of protected information (in bits) used in decision making. A quantitative view of fairness defects not only helps in ordering these defects, our empirical evaluation shows that it improves the search efficiency due to resulting smoothness of the search space. Guided by the quan-titative fairness, we present a causal debugging framework to localize inadequately trained layers and neurons responsible for fairness defects. Our experiments over ten DNNs, developed for socially critical tasks, show that Dice efficiently characterizes the amounts of discrimination, effectively generates discriminatory instances (vis-a-vis the state-of-the-art techniques), and localizes layers/neurons with significant biases.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{MonjeziTTT23,
title = {Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks},
author = {Verya Monjezi and Ashutosh Trivedi and Gang Tan and Saeid Tizpaz-Niari},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00136}
}