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dc.contributor.author
Beiro, Mariano Gastón
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dc.contributor.author
Kalimeri, Kyriaki
dc.date.available
2023-09-29T18:36:32Z
dc.date.issued
2022-09
dc.identifier.citation
Beiro, Mariano Gastón; Kalimeri, Kyriaki; Fairness in vulnerable attribute prediction on social media; Springer; Data Mining And Knowledge Discovery; 36; 6; 9-2022; 2194-2213
dc.identifier.issn
1384-5810
dc.identifier.uri
http://hdl.handle.net/11336/213661
dc.description.abstract
Historically, policymakers and practitioners relied exclusively on survey and census data to design and plan for assistive interventions; now, social media offer a timely and cost-effective way to reach out to populations otherwise unobserved. This study was designed to address the needs of a non-for-profit organisation to reach out to the young unemployed individuals in Italy with educational and job opportunities via communication channels that are more likely to appeal to younger generations. To this extend, we developed an ad-hoc Facebook application which administers questionnaires while gathering data about the Likes on Facebook Pages. Then, we developed a machine learning framework that successfully predicts the unemployment status of an unseen individual (.74 AUC). However, blindly delegating to the machine learning model the communication intervention may lead to digital discrimination on the basis of socio-demographic characteristics. Here, we propose a framework that aims to optimising both for the prediction performance as well as the most adequate fairness metric. Our framework is based on an adaptive threshold for gender, while we show that it can be expanded for other socio-demographic attributes and generalised for other interventions of assistive character. We present a doubly cross-validated setting that achieves out-of-sample stability and generalisability of results. We compare the behaviour of models that infer on different sets of data and provide an indepth discussion on the most predictive features, demonstrating that the “fairness through unawareness” approach does not suffice to achieve a fair classification since sensitive demographic information can be inferred not only via other sociodemographic attributes but also from behavioural digital patterns. Finally, we thoroughly assess the behaviour of the adaptive threshold approach and provide an in-depth discussion on the advantages but also the implications of such models offering actionable insights. Our results show that careful assessment of fairness metrics should be considered, primarily when AI models are employed for policymaking.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DATA FOR SOCIAL GOOD
dc.subject
DIGITAL DISCRIMINATION
dc.subject
FAIRNESS
dc.subject
MACHINE LEARNING
dc.subject
SOCIAL MEDIA
dc.subject
UNEMPLOYMENT
dc.subject.classification
Otras Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Fairness in vulnerable attribute prediction on social media
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-07-07T23:00:42Z
dc.journal.volume
36
dc.journal.number
6
dc.journal.pagination
2194-2213
dc.journal.pais
Alemania
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dc.journal.ciudad
Berlín
dc.description.fil
Fil: Beiro, Mariano Gastón. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long". Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long"; Argentina
dc.description.fil
Fil: Kalimeri, Kyriaki. Institute For Scientific Interchange Foundation; Italia
dc.journal.title
Data Mining And Knowledge Discovery
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s10618-022-00855-y
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s10618-022-00855-y
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