Assuring Data Authenticity in the Age of Generative AI
Abstract This commentary examines the challenges that generative artificial intelligence poses to the authenticity and integrity of scientific data. It proposes data-attestation requirements to strengthen accountability, preserve access to original research records, and help journals and institutions distinguish authentic evidence from synthetic or fabricated data.
Authors
- Jeff J. H. Kim (ORCID: https://orcid.org/0000-0003-4445-0890)
- Haavi Morreim
- David B. Resnik (ORCID: https://orcid.org/0000-0002-5139-9555)
- C. K. Gunsalus
- Mohammad Hosseini
Institutions
- University of Illinois Urbana-Champaign (US)
- University of Tennessee Health Science Center (US)
- University of Illinois Chicago (US)
- National Institute of Environmental Health Sciences (US)
Publication Details
- Journal
- The Journal of Law Medicine & Ethics
- Published
- 2026-09-01
- DOI
- https://doi.org/10.1017/jme.2026.10268
- Primary Topic
- Research Data Management Practices
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of Environmental Health Sciences
- National Center for Advancing Translational Sciences