ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development Using Natural Language Processing-Supported Human-Artificial Intelligence Collaboration
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md_146_5_051404.pdf
Description
Published version
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898.53 KB
Format
Adobe PDF
Checksum (MD5)
58108faea414c0832d8a871518f57ed2
Author(s)
Edwards, Kristen M
Song, Binyang
Porciello, Jaron
Engelbert, Mark
Huang, Carolyn
Ahmed, Faez
Date Issued
February 5, 2024
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Edwards, K. M., Song, B., Porciello, J., Engelbert, M., Huang, C., and Ahmed, F. (February 5, 2024). "ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development Using Natural Language Processing-Supported Human-Artificial Intelligence Collaboration." ASME. J. Mech. Des. May 2024; 146(5): 051404.
Version
Final published version
Abstract
When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an artificial intelligence (AI) agent based on a bidirectional encoder representations from transformers (BERT) model and incorporate it into a human team designing an evidence synthesis product for global development. We explore the effectiveness of the human–AI hybrid team in accelerating the evidence synthesis process. To further improve team efficiency, we enhance the human–AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies, including random sampling, least confidence (LC) sampling, and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent into the human team can reduce the human screening effort, i.e., the number of documents that humans need to screen, by 68.5% compared to the case of no AI assistance and by 16.8% compared to the industry-standard case of using a frequency-based language model and support vector machine-based classifier for identifying 80% of all relevant documents. When we apply the HP sampling strategy, the human screening effort can be reduced even more: by 78.3% for identifying 80% of all relevant documents compared to no AI assistance. We apply the AL-enhanced human–AI hybrid teaming workflow in the design process of three evidence gap maps for U.S. Agency for International Development and find it to be highly effective. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision-making in global development.
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DOI of Published Version
https://doi.org/10.1115/1.4064245
