Evaluating Gender Bias in Multilingual Multimodal AI Models: Insights from an Indian Context
Kshitish Ghate, Arjun Choudhry, Vanya Bannihatti Kumar · 2024
We evaluate gender biases in multilingual multimodal image and text models in two settings: text-to-image retrieval and text-to-image generation, to show that even seemingly genderneutral traits generate biased results.We evaluate our framework in the context of people from India, working with two languages: English and Hindi.We work with frameworks built around mCLIP-based models to ensure a thorough evaluation of recent state-of-the-art models in the multilingual setting due to their potential for widespread applications.We analyze the results across 50 traits for retrieval and 8 traits for generation, showing that current multilingual multimodal models are biased towards men for most traits, and this problem is further exacerbated for lower-resource languages like Hindi.We further discuss potential reasons behind this observation, particularly stemming from the bias introduced by the pretraining datasets.Our code can be found here.