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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bias mitigation of multimodal datasets in an urban-social category classifier

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Author(s):
Luciano C. Lugli ; Daniel Abujabra Merege ; Rafael Pillon Almeida
Total Authors: 3
Document type: Journal article
Source: Estudos avançados; v. 38, n. 111, p. 365-380, 2024-08-30.
Abstract

ABSTRACT This research project is based on the relational implications of the sociomoral development of Piaget’s psychogenetic theory on the cognition construction of ethics in personal biases as in references of discursive dialectics in linguistics. Functional data from training and testing were parameterized in an urban-social category classifier in a textual analytical approach by Natural Language Processing (NLP) and based on the Transformers adapted attention mechanism. In this perspective, a bias mitigation methodology was developed to restructure the convergence criteria in which multimodal datasets were retrained, retested, and reevaluated. Finally, the heterogeneity of the common collective human ethics was verified and validated, over interpretive inferences, insights, and real social trends, whereby the city/citizen relation addresses the “social sensing” in the identification of public-social problems. (AU)

FAPESP's process: 19/19032-6 - Daoura - Urban Insights Platform for Smart Cities
Grantee:Daniel Abujabra Merege
Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE