@inproceedings{bansal-etal-2022-well,
    title = "How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?",
    author = "Bansal, Hritik  and
      Yin, Da  and
      Monajatipoor, Masoud  and
      Chang, Kai-Wei",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.emnlp-main.88/",
    doi = "10.18653/v1/2022.emnlp-main.88",
    pages = "1358--1370",
    abstract = "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 \textit{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 CLIP-based and human evaluation on minDALL.E, DALL.E-mini and Stable Diffusion, we find that the model generations cover diverse social groups while preserving the image quality. In some cases, the generations would be anti-stereotypical (e.g., models tend to create images with individuals that are perceived as man when fed with prompts about makeup) in the presence of ethical intervention. 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."
}