Fairness in Forecasting and Learning Linear Dynamical Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00347618" target="_blank" >RIV/68407700:21230/21:00347618 - isvavai.cz</a>

Result on the web

<a href="https://arxiv.org/abs/2006.07315" target="_blank" >https://arxiv.org/abs/2006.07315</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Fairness in Forecasting and Learning Linear Dynamical Systems

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-866-4

ISSN

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e-ISSN

2374-3468

Number of pages

9

Pages from-to

11134-11142

Publisher name

Association for the Advancement of Artificial Intelligence (AAAI)

Place of publication

Palo Alto, California

Event location

Virtual Conference

Event date

Feb 2, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000681269802093