Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Authors = Assumpta Jover ORCID = 0000-0002-5406-2456

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
16 pages, 427 KiB  
Article
Dealing with Gender Bias Issues in Data-Algorithmic Processes: A Social-Statistical Perspective
by Juliana Castaneda, Assumpta Jover, Laura Calvet, Sergi Yanes, Angel A. Juan and Milagros Sainz
Algorithms 2022, 15(9), 303; https://doi.org/10.3390/a15090303 - 27 Aug 2022
Cited by 15 | Viewed by 10237
Abstract
Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a [...] Read more.
Are algorithms sexist? This is a question that has been frequently appearing in the mass media, and the debate has typically been far from a scientific analysis. This paper aims at answering the question using a hybrid social and technical perspective. First a technical-oriented definition of the algorithm concept is provided, together with a more social-oriented interpretation. Secondly, several related works have been reviewed in order to clarify the state of the art in this matter, as well as to highlight the different perspectives under which the topic has been analyzed. Thirdly, we describe an illustrative numerical example possible discrimination in the banking sector due to data bias, and propose a simple but effective methodology to address it. Finally, a series of recommendations are provided with the goal of minimizing gender bias while designing and using data-algorithmic processes to support decision making in different environments. Full article
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
Show Figures

Figure 1

Back to TopTop