提出可量化公平性的深度模型验证方法，提升准确率与速度。
FairQuant: Certifying and Quantifying Fairness of Deep Neural Networks
- 用符号区间分析结合迭代优化，实现对神经网络的公平性验证。
- 不仅能判断是否公平，还能计算出公平个体的比例。
- 在四个数据集上比现有方法快数个数量级，且更准确。
本文提出一种形式化认证与量化深度神经网络（DNN）个体公平性的方法。个体公平性要求在除受法律保护属性（如性别、种族）外其他特征相同的情况下，个体应获得相同对待。现有技术在面对大规模或高维输入的DNN时，普遍存在可扩展性差或准确性不足的问题。本方法通过符号区间分析并结合公平性引导的迭代精炼，克服了这一限制。同时，将传统的定性认证升级为定量认证，通过计算分类输出可被证明公平的个体占比，而非仅判断整体是否公平。我们在四个主流公平性研究数据集上实现了该方法，并进行了评估。实验结果表明，该方法不仅精度优于当前最先进方法，且效率提升数个数量级。
原文摘要 · Abstract (English)
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.
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