Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators.

ABSTRACT:

Importance

The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes.

Objective

To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees.

Design, setting, and participants

The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2

SUBMITTER: Ali R 

PROVIDER: S-EPMC10782243 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature