Systems to decrease gender bias in classifiers

Oier Irazabal Urrutia 2020

Finished

Research line:
Machine learning
Description:

It is said that with great power comes great responsibility. Nowadays, we rely on machine learning systems that are capable of understanding text at a human-like level. Yet, relations like "man is to computer scientist what woman is to homemaker" are present in these systems.

The importance of the topic and the effect it has in the society has made it become an important research topic during the last years giving rise to different solutions.

In this work, we describe some state-of-the-art techniques that reduce gender bias in machine learning algorithms as well as assess their results employing fairness evaluationmetrics.

Director(s):
Olatz Arbelaitz
University:
Euskal Herriko Unibertsitatea (UPV/EHU)
Center:
Informatika Fakultatea - Facultad de Informática
Department:
Konputagailuen Arkitektura eta Teknologia - Arquitectura y Tecnología de computadores
Reading date:
2020-07-02
Reading year:
2020