Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification
This addresses the need for computational fact-checking of generative AI outputs in biology, though it is incremental as it applies existing network methods to new AI-generated data.
The researchers tackled the problem of verifying disease-gene links generated by ChatGPT by comparing biological graphs from PubMed abstracts with those from ChatGPT-3.5 Turbo, achieving link accuracies ranging from 70% to 86%.
Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative abilities. Regulations and guidelines are emerging to verify generated contents and identify novel uses. we aspire to demonstrate how ChatGPT claims are checked computationally using the rigor of network models. We aim to achieve fact-checking of the knowledge embedded in biological graphs that were contrived from ChatGPT contents at the aggregate level. We adopted a biological networks approach that enables the systematic interrogation of ChatGPT's linked entities. We designed an ontology-driven fact-checking algorithm that compares biological graphs constructed from approximately 200,000 PubMed abstracts with counterparts constructed from a dataset generated using the ChatGPT-3.5 Turbo model. In 10-samples of 250 randomly selected records a ChatGPT dataset of 1000 "simulated" articles , the fact-checking link accuracy ranged from 70% to 86%. This study demonstrated high accuracy of aggregate disease-gene links relationships found in ChatGPT-generated texts.