Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators

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.