Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper investigates gender and skin-tone biases in Gemini Flash 2.5 Image and GPT Image 1.5 by generating 3,200 images from semantically neutral prompts. Using a pipeline involving color normalization, facial landmark masking, and skin tone quantification via Monk, PERLA, and Fitzpatrick scales, the study reveals a "default white" bias in both models. Gemini favored female-presenting subjects, while GPT favored male-presenting subjects with lighter skin tones, demonstrating that neutral prompts elicit polarized demographic defaults.
Despite using neutral prompts, Gemini and GPT image generators reveal stark "default white" biases, with one model skewing female and the other male, highlighting the persistence of demographic stereotypes in generative AI.
This study quantifies gender and skin-tone bias in two widely deployed commercial image generators - Gemini Flash 2.5 Image (NanoBanana) and GPT Image 1.5 - to test the assumption that neutral prompts yield demographically neutral outputs. We generated 3,200 photorealistic images using four semantically neutral prompts. The analysis employed a rigorous pipeline combining hybrid color normalization, facial landmark masking, and perceptually uniform skin tone quantification using the Monk (MST), PERLA, and Fitzpatrick scales. Neutral prompts produced highly polarized defaults. Both models exhibited a strong"default white"bias (>96% of outputs). However, they diverged sharply on gender: Gemini favored female-presenting subjects, while GPT favored male-presenting subjects with lighter skin tones. This research provides a large-scale, comparative audit of state-of-the-art models using an illumination-aware colorimetric methodology, distinguishing aesthetic rendering from underlying pigmentation in synthetic imagery. The study demonstrates that neutral prompts function as diagnostic probes rather than neutral instructions. It offers a robust framework for auditing algorithmic visual culture and challenges the sociolinguistic assumption that unmarked language results in inclusive representation.