Are AI systems biased against the poor?: a machine learning analysis using Word2Vec and GloVe embeddings
Curto, Georgina (Universitat Autònoma de Barcelona)
Jojoa Acosta, Mario Fernando (University of Deusto)
Comim, Flavio (Universitat Ramon Llull)
Garcia-Zapirain, Begoña (University of Deusto)
Eina, Centre Universitari de Disseny i Art de Barcelona

Data:	2022
Resum:	Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. This article contributes to the body of work on bias, both from and AI and a social sciences perspective, by providing evidence of a transversal aggravating factor for historical types of discrimination. The evidence of bias against the poor also has important consequences in terms of human development, since it often leads to discrimination, which constitutes an obstacle for the effectiveness of poverty reduction policies.
Nota:	First published: 28 June 2022
Nota:	Altres ajuts: Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The research leading to these results received partial funding support from the Aristos Campus Mundus Project.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Bias ; Artificial intelligence ; Embeddings ; Poverty
Publicat a:	AI & society, Vol. 39 (2024) , p. 617-632, ISSN 1435-5655

DOI: 10.1007/s00146-022-01494-z
PMID: 35789618

16 p, 1.2 MB

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