Kristen M. Edwards, Binyang Song, Jaron Porciello, Mark Engelbert, Carolyn Huang, Faez Ahmed
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Specifically, we explore different sampling strategies: random, least confidence (LC), and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent can reduce the human screening effort by 68.5% compared to the case of no AI assistance, and by 16.8% compared to using the industry standard model 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% 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 which are now published for USAID's use. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision making in global development.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development using Natural Language Processing-supported Human-AI Collaboration\",\"authors\":\"Kristen M. Edwards, Binyang Song, Jaron Porciello, Mark Engelbert, Carolyn Huang, Faez Ahmed\",\"doi\":\"10.1115/1.4064245\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n 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 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. We further enhance the human-AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies: random, least confidence (LC), and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent can reduce the human screening effort by 68.5% compared to the case of no AI assistance, and by 16.8% compared to using the industry standard model 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% 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 which are now published for USAID's use. 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ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development using Natural Language Processing-supported Human-AI Collaboration
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 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. We further enhance the human-AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies: random, least confidence (LC), and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent can reduce the human screening effort by 68.5% compared to the case of no AI assistance, and by 16.8% compared to using the industry standard model 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% 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 which are now published for USAID's use. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision making in global development.
期刊介绍:
The Journal of Mechanical Design (JMD) serves the broad design community as the venue for scholarly, archival research in all aspects of the design activity with emphasis on design synthesis. JMD has traditionally served the ASME Design Engineering Division and its technical committees, but it welcomes contributions from all areas of design with emphasis on synthesis. JMD communicates original contributions, primarily in the form of research articles of considerable depth, but also technical briefs, design innovation papers, book reviews, and editorials.
Scope: The Journal of Mechanical Design (JMD) serves the broad design community as the venue for scholarly, archival research in all aspects of the design activity with emphasis on design synthesis. JMD has traditionally served the ASME Design Engineering Division and its technical committees, but it welcomes contributions from all areas of design with emphasis on synthesis. JMD communicates original contributions, primarily in the form of research articles of considerable depth, but also technical briefs, design innovation papers, book reviews, and editorials.