Text-to-image generative models have achieved unprecedented success in
generating high-quality images based on natural language descriptions. However,
it is shown that these models tend to favor specific social groups when
prompted with neutral text descriptions (e.g., 'a photo of a lawyer').
Following Zhao et al. (2021), we study the effect on the diversity of the
generated images when adding ethical intervention that supports equitable
judgment (e.g., 'if all individuals can be a lawyer irrespective of their
gender') in the input prompts. To this end, we introduce an Ethical NaTural
Language Interventions in Text-to-Image GENeration (ENTIGEN) benchmark dataset
to evaluate the change in image generations conditional on ethical
interventions across three social axes -- gender, skin color, and culture.
Through ENTIGEN framework, we find that the generations from minDALL.E,
DALL.E-mini and Stable Diffusion cover diverse social groups while preserving
the image quality. Preliminary studies indicate that a large change in the
model predictions is triggered by certain phrases such as 'irrespective of
gender' in the context of gender bias in the ethical interventions. We release
code and annotated data at https://github.com/Hritikbansal/entigen_emnlp.Comment: 13 pages, 8 figures, 6 tables. Accepted as Oral Presentation at EMNLP
202