FairQuant：认证与量化深度神经网络的公平性
FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
AI总结：
本文提出FairQuant方法，通过对DNN符号区间分析应用抽象与迭代细化，克服可扩展性限制，将传统定性公平认证提升为定量认证，准确率更高且速度快数个数量级。
摘要：
我们提出了一种正式认证和量化深度神经网络（DNN）个体公平性的方法。个体公平性保证任何两个除了受法律保护属性（例如性别或种族）外完全相同的个体受到相同对待。尽管现有技术能提供此类保证，但随着DNN规模和输入维度的增加，它们往往缺乏可扩展性或准确性。我们的方法通过对DNN的符号区间分析应用抽象，随后进行由公平性属性引导的迭代细化，从而克服了这一限制。此外，通过计算分类输出可证明公平的个体百分比，而非仅仅判定DNN是否公平，我们的方法将基于符号区间的分析从传统的定性认证提升为定量认证。我们实现了该方法，并在四个流行的公平性研究数据集上训练的深度神经网络进行了评估。实验结果表明，我们的方法不仅比最先进的技术更准确，而且快了几个数量级。
英文摘要：
We propose a method for formally certifying and quantifying individual fairness of deep neural networks (DNN). Individual fairness guarantees that any two individuals who are identical except for a legally protected attribute (e.g., gender or race) receive the same treatment. While there are existing techniques that provide such a guarantee, they tend to suffer from lack of scalability or accuracy as the size and input dimension of the DNN increase. Our method overcomes this limitation by applying abstraction to a symbolic interval based analysis of the DNN followed by iterative refinement guided by the fairness property. Furthermore, our method lifts the symbolic interval based analysis from conventional qualitative certification to quantitative certification, by computing the percentage of individuals whose classification outputs are provably fair, instead of merely deciding if the DNN is fair. We have implemented our method and evaluated it on deep neural networks trained on four popular fairness research datasets. The experimental results show that our method is not only more accurate than state-of-the-art techniques but also several orders-of-magnitude faster.
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。
