Fairness in Forecasting and Learning Linear Dynamical Systems

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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Cited by in corpus (4)

• TSSOS: a Julia library to exploit sparsity for large-scale polynomial optimization
• Fairness in Forecasting of Observations of Linear Dynamical Systems
• Exploiting term sparsity in Noncommutative Polynomial Optimization
• SparseJSR: A Fast Algorithm to Compute Joint Spectral Radius via Sparse SOS Decompositions