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Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health. This work yielded a digital knowledge base of entities, topics, and documents that can be used to probe the inner workings of the Human Genome Project, a massive international public-private effort to sequence the human genome, and several of its offshoots like The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE). The resulting knowledge base will be instrumental in understanding not only how the Human Genome Project and genomics research developed collaboratively, but also how scientific goals come to be formulated and evolve. Given the diverse and rich data used in this project, we evaluated the ethical implications of employing AI and LLMs to process and analyze this valuable archive. As the first computational investigation of the internal archives of a massive collaborative project with multiple funders and institutions, this study will inform future efforts to conduct similar investigations while also considering and minimizing ethical challenges. Our methodology and risk-mitigating measures could also inform future initiatives in developing standards for project planning, policymaking, enhancing transparency, and ensuring ethical utilization of artificial intelligence technologies and large language models in archive exploration.Author Contributions: Mohammad Hosseini: Investigation; Project Administration; Writing – original draft; Writing – review & editing. Spencer Hong: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Thomas Stoeger: Conceptualization; Investigation; Project Administration; Supervision; Writing – original draft; Writing – review & editing. Kristi Holmes: Funding acquisition, Supervision, Writing – review & editing. Luis A. Nunes Amaral: Funding acquisition, Supervision, Writing – review & editing. Christopher Donohue: Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. Kris Wetterstrand: Conceptualization, Funding acquisition, Project administration.
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This content will become publicly available on August 1, 2025
Empathy and AI: Achieving Equitable Microtransit for Underserved Communities
This paper describes a newly launched project that will produce a new approach to public microtransit for underserved communities. Public microtransit cannot rely on pricing signals to manage demand, and current approaches face the challenges of simultaneously being underutilized and overextended. This project conceives of the setting as a sociotechnical system. Its main idea is to engage users through AI agents in conjunction with platform constraints to find solutions that purely technical conceptions cannot find. The project was specified over an intense series of discussions with key stakeholders (riders, city government, and nongovernmental agencies) and brings together expertise in the disciplines of AI, Operations Research, Urban Planning, Psychology, and Community Development. The project will culminate in a pilot study, results from which will facilitate the transfer of its technology to additional communities.
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- Award ID(s):
- 2325720
- PAR ID:
- 10538107
- Publisher / Repository:
- International Joint Conferences on Artificial Intelligence Organization
- Date Published:
- Volume:
- 30
- ISBN:
- 978-1-956792-04-1
- Page Range / eLocation ID:
- 7179 to 7187
- Format(s):
- Medium: X
- Location:
- Jeju, South Korea
- Sponsoring Org:
- National Science Foundation
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