Measuring unfairness through game-theoretic interpretability

Juliana Cesaro, Fabio Gagliardi Cozman

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

One often finds in the literature connections between measures of fairness and measures of feature importance employed to interpret trained classifiers. However, there seems to be no study that compares fairness measures and feature importance measures. In this paper we propose ways to evaluate and compare such measures. We focus in particular on SHAP, a game-theoretic measure of feature importance; we present results for a number of unfairness-prone datasets.

Idioma originalInglés
Título de la publicación alojadaMachine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
EditoresPeggy Cellier, Kurt Driessens
EditorialSpringer Verlag
Páginas253-264
Número de páginas12
ISBN (versión impresa)9783030438227
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Alemania
Duración: 16 sep 201920 sep 2019

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1167 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
País/TerritorioAlemania
CiudadWurzburg
Período16/09/1920/09/19

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