The importance of addressing fairness and bias in artificial intelligence (AI) systems cannot be over-emphasized. Mainstream media has been awashed with news of incidents around stereotypes and bias in many of these systems in recent years. In this survey, we fill a gap with regards to the minimal study of fairness and bias in Large Multimodal Models (LMMs) compared to Large Language Models (LLMs), providing 50 examples of datasets and models along with the challenges affecting them; we identify a new category of quantifying bias (preuse), in addition to the two well-known ones in the literature: intrinsic and extrinsic; we critically discuss the various ways researchers are addressing these challenges. Our method involved two slightly different search queries on Google Scholar, which revealed that 33,400 and 538,000 links are the results for the terms "Fairness and bias in Large Multimodal Models" and "Fairness and bias in Large Language Models", respectively. We believe this work contributes to filling this gap and providing insight to researchers and other stakeholders on ways to address the challenge of fairness and bias in multimodal A!.

翻译：解决人工智能（AI）系统中的公平性与偏见问题的重要性无论怎样强调都不为过。近年来，主流媒体充斥着关于许多此类系统中存在的刻板印象与偏见事件的新闻。在本综述中，我们填补了相较于大型语言模型（LLMs），针对大型多模态模型（LMMs）中公平性与偏见的研究尚显不足的空白，提供了50个数据集与模型的示例及其面临的挑战；我们在文献中已广为人知的两类量化偏见方法（内在的与外在的）之外，识别出了一类新的量化偏见类别（预使用）；我们批判性地讨论了研究人员应对这些挑战的各种方式。我们的方法涉及在Google Scholar上执行两个略有差异的检索查询，结果显示，针对术语"Fairness and bias in Large Multimodal Models"和"Fairness and bias in Large Language Models"的检索结果链接数分别为33,400条和538,000条。我们相信这项工作有助于填补这一空白，并为研究人员及其他利益相关者提供关于如何应对多模态AI中公平性与偏见挑战的见解。