  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!.
