Beyond Western Bias: IndicVisionBench Exposes VLM Gaps in Culturally Diverse & Multilingual Settings
By Ali Faraz, Akash, Shaharukh Khan, Raja Kolla, Akshat Patidar, Suranjan Goswami, Abhinav Ravi, Chandra Khatri, Shubham Agarwal
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary
A new benchmark, IndicVisionBench, has been introduced to address the Western-centric bias prevalent in Vision-Language Model (VLM) evaluations. This groundbreaking, large-scale benchmark focuses specifically on the Indian subcontinent, encompassing English and 10 Indian languages. It evaluates VLMs across three critical multimodal tasks: Optical Character Recognition (OCR), Multimodal Machine Translation (MMT), and Visual Question Answering (VQA), covering six distinct question types. The benchmark comprises approximately 5,000 images and over 37,000 QA pairs, grounded in 13 culturally relevant topics. It also releases a unique parallel corpus to facilitate the analysis of cultural and linguistic biases in VLMs. Through evaluations of eight diverse VLM models-including proprietary and open-source systems-the research uncovered substantial performance gaps, unequivocally highlighting the limitations of current VLMs in culturally and linguistically diverse environments and advocating for more inclusive multimodal AI research.
Why It Matters
This research is a crucial wake-up call for the entire AI industry, particularly for professionals involved in VLM development, deployment, and AI ethics. The findings from IndicVisionBench highlight a critical, yet often overlooked, challenge: the pervasive Western-centric bias embedded in current VLM evaluation benchmarks and, consequently, in the models themselves. For AI professionals, this isn't merely an academic concern; it has profound implications across several fronts. Firstly, it underscores a fundamental flaw in the promise of "general artificial intelligence" if models cannot reliably perform in culturally and linguistically diverse settings. As AI systems become more integrated into global societies, models that falter outside Western contexts risk alienating vast user bases, perpetuating digital inequality, and leading to suboptimal or even harmful outcomes for billions of people. Secondly, from a commercial perspective, ignoring these biases means missing massive market opportunities in rapidly growing non-Western economies. Companies aiming for global adoption need models that can genuinely understand and interact with diverse cultures, languages, and visual nuances. Thirdly, this benchmark serves as a blueprint for a more responsible and inclusive AI development paradigm. It pushes developers to move beyond a narrow scope of data and evaluation, encouraging the creation of truly robust, equitable, and globally competent VLMs. Professionals must now actively prioritize diverse data collection, culturally sensitive annotation, and multilingual architectural design to build AI that truly serves humanity, not just a segment of it, and avoid the significant reputational and ethical pitfalls of culturally tone-deaf AI.