Fairness and Bias in Multimodal AI: A Survey

TL;DR

This survey examines fairness and bias issues in multimodal AI, highlighting dataset challenges, mitigation strategies, and the relatively under-studied area of preprocessing methods, aiming to guide future research and stakeholder efforts.

Contribution

It provides a comprehensive review of bias in multimodal models, introduces a new focus on preprocessing mitigation, and offers a curated set of datasets and models for future study.

Findings

Bias is less studied in multimodal AI compared to language models.

Preprocessing mitigation methods are under-explored in current research.

The survey offers a curated list of datasets and models related to fairness.

Abstract

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 other types of bias in many of these systems in recent years. In this survey, we fill a gap with regards to the relatively minimal study of fairness and bias in Large Multimodal Models (LMMs) compared to Large Language Models (LLMs), providing 50 examples of datasets and models related to both types of AI along with the challenges of bias affecting them. We discuss the less-mentioned category of mitigating bias, preprocessing (with particular attention on the first part of it, which we call preuse). The method is less-mentioned compared to the two well-known ones in the literature: intrinsic and extrinsic mitigation methods. We critically discuss the various ways researchers are addressing these challenges. Our method involved two slightly different search queries on two reputable search engines, Google Scholar and Web of Science (WoS), which revealed that for the queries 'Fairness and bias in Large Multimodal Models' and 'Fairness and bias in Large Language Models', 33,400 and 538,000 links are the initial results, respectively, for Scholar while 4 and 50 links are the initial results, respectively, for WoS. For reproducibility and verification, we provide links to the search results and the citations to all the final reviewed papers. We believe this work contributes to filling this gap and providing insight to researchers and other stakeholders on ways to address the challenges of fairness and bias in multimodal and language AI.