Fairness in Forecasting of Observations of Linear Dynamical Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00368296" target="_blank" >RIV/68407700:21230/23:00368296 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1613/jair.1.14050" target="_blank" >https://doi.org/10.1613/jair.1.14050</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1613/jair.1.14050" target="_blank" >10.1613/jair.1.14050</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fairness in Forecasting of Observations of Linear Dynamical Systems

Popis výsledku v původním jazyce

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in timeseries forecasting problems: subgroup fairness and instantaneous fairness. These notion extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.

Název v anglickém jazyce

Fairness in Forecasting of Observations of Linear Dynamical Systems

Popis výsledku anglicky

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in timeseries forecasting problems: subgroup fairness and instantaneous fairness. These notion extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of Artificial Intelligence Research

ISSN

1076-9757

e-ISSN

1943-5037

Svazek periodika

76

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

34

Strana od-do

1247-1280

Kód UT WoS článku

000982549100001

EID výsledku v databázi Scopus

2-s2.0-85160287419