The rapid advancement of artificial intelligence (AI) technology poses significant ethical challenges, particularly when AI systems are trained on biased data, leading to biased outcomes with real-world consequences. To address these concerns, a team of researchers at the National Institute of Standards and Technology (NIST) proposes drawing from historical precedent in human subjects research to establish ethical guidelines for AI development.
In their recent publication in the journal Computer, the researchers advocate applying the fundamental principles outlined in the Belmont Report of 1979 to AI research. These principles—respect for persons, beneficence, and justice—have long served as the cornerstone of ethical human subjects research and have influenced U.S. government policies in this domain.
Kristen Greene, a social scientist at NIST and one of the paper’s authors, emphasizes the importance of transparency in AI research, particularly regarding the use of individuals’ data to train AI systems. By adhering to established ethical paradigms, researchers can ensure accountability and safeguard the rights of research participants.
The Belmont Report emerged in response to unethical research practices, such as the notorious Tuskegee syphilis study, underscoring the imperative to protect human subjects in biomedical and behavioral research. The subsequent establishment of the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research led to the codification of ethical principles in the Common Rule of 1991.
While the Common Rule applies primarily to government-funded research, the NIST researchers advocate for broader adoption of its principles across all research involving human subjects, including AI development. This expansion would address ethical concerns arising from the use of data sourced from individuals who have not consented to its utilization—a violation of the “respect for persons” principle.
As AI technology continues to advance, it is imperative to establish robust ethical frameworks to guide its development and deployment. By drawing from historical precedent and applying established ethical principles, researchers can promote transparency, accountability, and responsible AI innovation.
