Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators
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Other documents of the author: García-Esparza, Juan A.; Pardo, Javier; Altaba, Pablo; Alberich, Mario
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comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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INVESTIGACIONMetadata
Title
Validity of Machine Learning in Assessing Large Texts Through Sustainability IndicatorsDate
2023Publisher
SpringerBibliographic citation
GARCÍA-ESPARZA, Juan A., et al. Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators. Social Indicators Research, 2023, vol. 166, no 2, p. 323-337.Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s11205-023-03075-zVersion
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Abstract
As machine learning becomes more widely used in policy and environmental impact settings, concerns about accuracy and fairness arise. These concerns have piqued the interest
of researchers, who have advanced new ... [+]
As machine learning becomes more widely used in policy and environmental impact settings, concerns about accuracy and fairness arise. These concerns have piqued the interest
of researchers, who have advanced new approaches and theoretical insights to enhance
data gathering, treatment and models’ training. Nonetheless, few works have looked at the
trade-offs between appropriateness and accuracy in indicator evaluation to comprehend
how these constraints and approaches may better redound into policymaking and have a
more significant impact across culture and sustainability matters for urban governance.
This empirical study fulfils this void by researching indicators’ accuracy and utilizing
algorithmic models to test the benefits of large text-based analysis. Here we describe applied work in which we find affinity and occurrence in indicators trade-offs that result be
significant in practice to evaluate large texts. In the study, objectivity and fairness are kept
substantially without sacrificing accuracy, explicitly focusing on improving the processing
of indicators to be truthfully assessed. This observation is robust when cross-referring indicators and unique words. The empirical results advance a novel form of large text analysis through machine intelligence and refute a widely held belief that artificial intelligence
text processing necessitates either accepting a significant reduction in accuracy or fairness. [-]
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Social Indicators Research, 2023, vol. 166, no 2, p. 323-337.Funder Name
CRUE-CSIC | Springer Nature
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© The Author(s) 2023
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