Fairness in Forecasting and Learning Linear Dynamical Systems
This work addresses fairness issues in forecasting for applications like insurance, though it is incremental as it builds on existing fairness and optimization frameworks.
The paper tackles under-representation bias in time-series forecasting by introducing subgroup and instantaneous fairness notions for learning linear dynamical systems from multiple trajectories, providing globally convergent methods and showing improved statistical performance on biased datasets like COMPAS.
In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. When the amounts of training data for the subgroups are not controlled carefully, under-representation bias arises. We introduce two natural notions of subgroup fairness and instantaneous fairness to address such under-representation bias in time-series forecasting problems. In particular, we consider the subgroup-fair and instant-fair learning of a linear dynamical system (LDS) from multiple trajectories of varying lengths, and the associated forecasting problems. We provide globally convergent methods for the learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate both the beneficial impact of fairness considerations on statistical performance and encouraging effects of exploiting sparsity on run time.