A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power

TL;DR

This study evaluates 20 AI text-to-image models for communicating nuclear energy concepts to the public, highlighting their strengths and limitations in accurately representing technical details and biases.

Contribution

It provides a comparative analysis of generative AI models' effectiveness in scientific communication about nuclear energy, identifying key shortcomings and areas for improvement.

Findings

DALL-E, DreamStudio, and Craiyon perform well on general nuclear prompts.

Models struggle with technical accuracy and bias reproduction.

Indigenous landscapes are poorly represented in generated images.

Abstract

In this work, we propose and assess the potential of generative artificial intelligence (AI) to generate public engagement around potential clean energy sources. Such an application could increase energy literacy -- an awareness of low-carbon energy sources among the public therefore leading to increased participation in decision-making about the future of energy systems. We explore the use of generative AI to communicate technical information about low-carbon energy sources to the general public, specifically in the realm of nuclear energy. We explored 20 AI-powered text-to-image generators and compared their individual performances on general and scientific nuclear-related prompts. Of these models, DALL-E, DreamStudio, and Craiyon demonstrated promising performance in generating relevant images from general-level text related to nuclear topics. However, these models fall short in three crucial ways: (1) they fail to accurately represent technical details of energy systems; (2) they reproduce existing biases surrounding gender and work in the energy sector; and (3) they fail to accurately represent indigenous landscapes -- which have historically been sites of resource extraction and waste deposition for energy industries. This work is performed to motivate the development of specialized generative tools and their captions to improve energy literacy and effectively engage the public with low-carbon energy sources.