Simulated misuse of large language models and clinical credit systems

James T Anibal; Hannah B Huth; Jasmine Gunkel; Susan K Gregurick; Bradford J Wood

doi:10.1038/s41746-024-01306-2

Simulated misuse of large language models and clinical credit systems

NPJ Digit Med. 2024 Nov 11;7(1):317. doi: 10.1038/s41746-024-01306-2.

Authors

James T Anibal¹, Hannah B Huth², Jasmine Gunkel³, Susan K Gregurick⁴, Bradford J Wood²

Affiliations

¹ Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, MD, USA. [email protected].
² Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, MD, USA.
³ Department of Bioethics, National Institutes of Health (NIH), Bethesda, MD, USA.
⁴ Office of the Director, National Institutes of Health (NIH), Bethesda, MD, USA.

Abstract

In the future, large language models (LLMs) may enhance the delivery of healthcare, but there are risks of misuse. These methods may be trained to allocate resources via unjust criteria involving multimodal data - financial transactions, internet activity, social behaviors, and healthcare information. This study shows that LLMs may be biased in favor of collective/systemic benefit over the protection of individual rights and could facilitate AI-driven social credit systems.

Publication types

Review

Abstract

Publication types

Grants and funding