Fairness in Forecasting and Learning Linear Dynamical Systems

• URL: http://arxiv.org/abs/2006.07315v2
• Date: Sat, 2 Jan 2021 12:28:19 GMT
• Title: Fairness in Forecasting and Learning Linear Dynamical Systems
• Authors: Quan Zhou, Jakub Marecek, Robert N. Shorten
• Abstract summary: 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 from multiple trajectories of varying lengths.
• Score: 10.762748665074794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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