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Fairness in Forecasting and Learning Linear Dynamical Systems
- Source :
- Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
- Publication Year :
- 2020
-
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.
Details
- Database :
- arXiv
- Journal :
- Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
- Publication Type :
- Report
- Accession number :
- edsarx.2006.07315
- Document Type :
- Working Paper