@inproceedings{bb7189fad972450b86dfe8015180b66a,
title = "Cultural Relevance Index: Measuring Cultural Relevance in AI-Generated Images",
abstract = "This paper introduces the Cultural Relevance Index (CRI), a metric designed to evaluate and quantify the cultural relevance of AI-generated images. Leveraging the detection capabilities of GPT technology and our proposed mathematical formulas aligned with human perception, CRI assesses the extent to which the content of an image is relevant to a particular culture such as Arabic, a capability not commonly found in many AI technologies. Through rigorous validation, including comparison with human judgments and a mathematical baseline, with focus on Arabic culture, CRI demonstrates a compelling similarity (around 95\%) with human judgement, outperforming a quantified baseline metric (over 28\%). CRI also succeeds to matches human judgment in comparing cultural images 100\% of the time.",
keywords = "AI-generated images, Arabic culture, Cultural Relevance, Human assessment",
author = "Wala Elsharif and Marco Agus and Mahmoud Alzubaidi and James She",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 7th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2024 ; Conference date: 07-08-2024 Through 09-08-2024",
year = "2024",
month = oct,
day = "15",
doi = "10.1109/MIPR62202.2024.00071",
language = "English",
isbn = "979-8-3503-5143-9",
series = "Ieee Conference On Multimedia Information Processing And Retrieval",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "410--416",
booktitle = "2024 Ieee 7th International Conference On Multimedia Information Processing And Retrieval, Mipr 2024",
address = "United States",
}