Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification

TL;DR

This study presents an ontology-driven method to fact-check biological knowledge generated by ChatGPT by comparing it with curated biological graphs, achieving high accuracy in disease-gene link verification.

Contribution

It introduces a novel computational framework for verifying ChatGPT's biological claims using network models and ontology-based algorithms.

Findings

Achieved 70-86% link accuracy in fact-checking ChatGPT-generated data.

Demonstrated high accuracy in verifying disease-gene relationships.

Validated the use of biological graphs for AI content verification.

Abstract

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.