Visualizing Climate Change in an Era of A.I. Slop: How Chatbot Image Generator Models Distort the Climate Crisis in Public Imagination(s)

Climate change has long been difficult to visualize, contributing to climate inaction. Critical visual methods are used to analyze the social constructions of climate change encoded within leading generative A.I. chatbot text-to-image large language models: OpenAI's DALL·E 3 and Google Gemini's Imagen 2 and Imagen 3. Synthetic data for two types of generative A.I. climate change imagery are examined: (1) still images generated using generic climate change prompts and (2) images generated about heat wave impacts on people. Findings show that polar bears are a consistent visual metaphor for the climate crisis in images created with DALL·E 3 and that the model distorts climate change extreme heat risks. Google Gemini's Imagen models generated more photorealistic climate visuals somewhat grounded in climate science with greater safeguards built-in for the generation of humanoid figures and depictions of human suffering. As this research shows, generative A.I. visual outputs are reflective of the biases actively encoded into text-to-image models through data training sets and programming decisions. It is argued that chatbot image generator models distort the climate crisis in public imaginations by replicating pre-existing visual (mis-)representations of climate risks.