Mark Hernandez, East Tennessee State University Quillen College of Medicine
Emilia Calvaresi, Cleveland Clinic
Chhavi Chauhan, American Society for Investigative Pathology
Hannah Damico, Van Andel Institute
Darla P. Henderson, Federation of American Societies for Experimental Biology
Courtney Karner, University of Texas Southwestern
Frank Krause, Federation of American Societies for Experimental Biology
Robin G. Lorenz, Genentech
Zeynep Madak-Erdogan, University of Illinois Cancer Center
Naim Matasci, Ellison Medical Institute
Sally Schwettmann, Federation of American Societies for Experimental Biology
H. Joseph Yost, The Catholic University of America
Jennifer Zeitzer, Federation of American Societies for Experimental Biology
Purpose
In 2024, FASEB established a task force, to establish recommendations for federal agencies, policy makers, scientific societies, and researchers about the impact of Generative AI (Gen AI). The objectives were to: 1) develop recommendations on the appropriate and responsible use of Gen AI in routine research activities related to the biomedical/biological research community and 2) identify potential FASEB and society applications of Gen AI that can help achieve individual society missions, improve editorial workflow efficiencies, expand member engagement, and increase workforce productivity.
Methods
Members from 9 different FASEB societies and external experts representing perspectives from industry, academics, and institutes joined this task force and two reports were developed.
Results
The first report includes findings and recommendations targeted at five key stakeholder groups: 1) US federal agencies, 2) policy makers (including federal legislative and executive branches), 3) institutions (research, education, and corporate), 4) scholarly societies, and 5) individual researchers. Five key recommendation themes highlight the positive benefits of Gen AI while mitigating the potential negative impacts, ensuring research integrity, and respecting individual rights. These are: 1) Policy and Regulation; 2) Scientific Integrity and Intellectual Property; 3) Data Privacy and Security; 4) Diversity, Equity, Inclusion, and Accessibility (DEIA); and 5) Workforce Impact, Training, and Education. A second report includes additional recommendations, guidance, and resources for societies. These recommendations focus on 1) increasing knowledge, training, and educational opportunities for society leaders, staff, and members; 2) developing a Gen AI policy, strategy, and vision; 3) addressing security and fostering trust and transparency; and 4) considering DEIA and collaboration opportunities.
Conclusions
Gen AI is a transformative technology with potential for significant benefits to all stakeholders; however it may exacerbate existing challenges and/or present new ones if not implemented ethically. Stakeholder collaboration is necessary to address these challenges and achieve desired positive benefits of Gen AI.