Fairness in Forecasting and Learning Linear Dynamical Systems

Understand

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.