论文标题
超越个人和团体公平
Beyond Individual and Group Fairness
论文作者
论文摘要
我们提出了一种新的数据驱动的公平模型，与现有的个人或团体公平的静态定义不同，该模型受系统收到的不公平投诉的指导。我们的模型支持多个公平标准，并考虑到其潜在的不兼容。我们考虑了我们模型的随机和对抗性环境。在随机环境中，我们表明我们的框架可以自然地作为马尔可夫决策过程，并具有随机损失，为此我们提供了有效消失的遗憾算法解决方案。在对抗环境中，我们设计具有竞争比保证的有效算法。我们还报告了通过算法和人工数据集上的随机框架的实验结果，以凭经验证明它们的有效性。
We present a new data-driven model of fairness that, unlike existing static definitions of individual or group fairness is guided by the unfairness complaints received by the system. Our model supports multiple fairness criteria and takes into account their potential incompatibilities. We consider both a stochastic and an adversarial setting of our model. In the stochastic setting, we show that our framework can be naturally cast as a Markov Decision Process with stochastic losses, for which we give efficient vanishing regret algorithmic solutions. In the adversarial setting, we design efficient algorithms with competitive ratio guarantees. We also report the results of experiments with our algorithms and the stochastic framework on artificial datasets, to demonstrate their effectiveness empirically.
