@inproceedings{khullar-etal-2025-hateimgprompts,
    title = "{H}ate{I}mg{P}rompts: Mitigating Generation of Images Spreading Hate Speech",
    author = "Khullar, Vineet Kumar  and
      Velugubantla, Venkatesh  and
      Reddy Rella, Bhanu Prakash  and
      Mannava, Mohan Krishna  and
      Sathvik, Msvpj",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Bizzoni, Yuri  and
      Miyagawa, So  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
    month = may,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nlp4dh-1.53/",
    pages = "647--652",
    ISBN = "979-8-89176-234-3",
    abstract = "The emergence of artificial intelligence has proven beneficial to numerous organizations, particularly in its various applications for social welfare. One notable application lies in AI-driven image generation tools. These tools produce images based on provided prompts. While this technology holds potential for constructive use, it also carries the risk of being exploited for malicious purposes, such as propagating hate. To address this we propose a novel dataset {\textquotedblleft}HateImgPrompts{\textquotedblright}. We have benchmarked the dataset with the latest models including GPT-3.5, LLAMA 2, etc. The dataset consists of 9467 prompts and the accuracy of the classifier after finetuning of the dataset is around 81{\%}."
}