Journal article

Investigating Gender and Racial Biases in DALL-E Mini Images

Marc Cheong, Ehsan Abedin, Marinus Ferreira, Ritsaart Reimann, Shalom Chalson, Pamela Robinson, Joanne Byrne, Leah Ruppanner, Mark Alfano, Colin Klein

ACM Journal on Responsible Computing | Association for Computing Machinery (ACM) | Published : 2024

Open access

Abstract

Generative artificial intelligence systems based on transformers, including both text generators such as GPT-4 and image generators such as DALL-E 3, have recently entered the popular consciousness. These tools, while impressive, are liable to reproduce, exacerbate, and reinforce extant human social biases, such as gender and racial biases. In this article, we systematically review the extent to which DALL-E Mini suffers from this problem. In line with the Model Card published alongside DALL-E Mini by its creators, we find that the images it produces tend to represent dozens of different occupations as populated either solely by men (e.g., pilot, builder, plumber) or solely by women (e.g., h..

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