Gender Bias in Multimodal Models: A Transnational Feminist Approach Considering Geographical Region and Culture

Autor:	Mandal, Abhishek, Little, Suzanne, Leavy, Susan
Rok vydání:	2023
Předmět:	Computer Science - Computers and Society
Druh dokumentu:	Working Paper
Popis:	Deep learning based visual-linguistic multimodal models such as Contrastive Language Image Pre-training (CLIP) have become increasingly popular recently and are used within text-to-image generative models such as DALL-E and Stable Diffusion. However, gender and other social biases have been uncovered in these models, and this has the potential to be amplified and perpetuated through AI systems. In this paper, we present a methodology for auditing multimodal models that consider gender, informed by concepts from transnational feminism, including regional and cultural dimensions. Focusing on CLIP, we found evidence of significant gender bias with varying patterns across global regions. Harmful stereotypical associations were also uncovered related to visual cultural cues and labels such as terrorism. Levels of gender bias uncovered within CLIP for different regions aligned with global indices of societal gender equality, with those from the Global South reflecting the highest levels of gender bias. Comment: Selected for publication at the Aequitas 2023: Workshop on Fairness and Bias in AI | co-located with ECAI 2023, Krak\'ow, Poland
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.04997 Zobrazit plný text záznamu View this record from Arxiv