@inproceedings{velugubantla-etal-2025-ai,
    title = "{AI} Tools Can Generate Misculture Visuals! Detecting Prompts Generating Misculture Visuals For Prevention",
    author = "Velugubantla, Venkatesh  and
      Sonani, Raj  and
      Sathvik, Msvpj",
    editor = "Atwell, Katherine  and
      Biester, Laura  and
      Borah, Angana  and
      Dementieva, Daryna  and
      Ignat, Oana  and
      Kotonya, Neema  and
      Liu, Ziyi  and
      Wan, Ruyuan  and
      Wilson, Steven  and
      Zhao, Jieyu",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.nlp4pi-1.25/",
    doi = "10.18653/v1/2025.nlp4pi-1.25",
    pages = "285--293",
    ISBN = "978-1-959429-19-7",
    abstract = "Advanced AI models that generate realistic images from text prompts offer new creative possibilities but also risk producing culturally insensitive or offensive content. To address this issue, we introduce a novel dataset designed to classify text prompts that could lead to the generation of harmful images misrepresenting different cultures and communities. By training machine learning models on this dataset, we aim to automatically identify and filter out harmful prompts before image generation, balancing cultural sensitivity with creative freedom. Benchmarking with state-ofthe-art language models, our baseline models achieved an accuracy of 73.34{\%}."
}