Leveraging AI and Data Visualization for Enhanced Policy-Making: Aligning Research Initiatives with Sustainable Development Goals

Scientists, research institutions, funding agencies, and policy-makers have all emphasized the need to monitor and prioritize research investments and outputs to support the achievement of the United Nations Sustainable Development Goals (SDGs). Unfortunately, many current and historic research publ...

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Vydáno v:Sustainability Ročník 16; číslo 24; s. 11050
Hlavní autoři: Lino Ferreira da Silva Barros, Maicon Herverton, Medeiros Neto, Leonides, Santos, Guto Leoni, Leal, Roberto Cesar da Silva, Leal da Silva, Raysa Carla, Lynn, Theo, Dourado, Raphael Augusto, Endo, Patricia Takako
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2024
Témata:
Artificial intelligence
Datasets
Decision making
Large language models
Methods
Policy sciences
Sustainable development
Technology application
Brazil
ISSN:2071-1050, 2071-1050
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Shrnutí:Scientists, research institutions, funding agencies, and policy-makers have all emphasized the need to monitor and prioritize research investments and outputs to support the achievement of the United Nations Sustainable Development Goals (SDGs). Unfortunately, many current and historic research publications, proposals, and grants were not categorized against the SDGs at the time of submission. Manual post hoc classification is time-consuming and prone to human biases. Even when classified, few tools are available to decision makers for supporting resource allocation. This paper aims to develop a deep learning classifier for categorizing research abstracts by the SDGs and a decision support system for research funding policy-makers. First, we fine-tune a Bidirectional Encoder Representations from Transformers (BERT) model using a dataset of 15,488 research abstracts from authors at leading Brazilian universities, which were preprocessed and balanced for training and testing. Second, we present a PowerBI dashboard that visualizes classifications for supporting informed resource allocation for sustainability-focused research. The model achieved an F1-score, precision, and recall exceeding 70% for certain classes and successfully classified existing projects, thereby enabling better tracking of Agenda 2030 progress. Although the model is capable of classifying any text, it is specifically optimized for Brazilian research due to the nature of its fine-tuning data.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su162411050