论文标题
预测线性动力学系统观察的公平性
Fairness in Forecasting of Observations of Linear Dynamical Systems
论文作者
论文摘要
在机器学习中，培训数据通常捕获一些基本人口的多个亚组的行为。这种行为通常可以建模为对未知状态的未知动力系统的观察。但是，当子组的训练数据不仔细控制时，会产生代表性不足的偏差。为了应对代表性不足的偏见，我们在预测问题上介绍了两个自然的公平概念：亚组公平和瞬时公平。这些概念将预测奇偶性扩展到了动态系统的学习。我们还使用非交通性多项式优化问题的既定层次结构显示了全球收敛的方法，以解决公平受限的学习问题。我们还表明，通过利用凸的稀疏性，我们可以大大减少方法的运行时间。我们对由保险应用程序和众所周知的Compas数据集的有偏见数据集的经验结果证明了我们方法的功效。
In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in time-series forecasting problems: subgroup fairness and instantaneous fairness. These notions extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.
