The Performance and Representation Gap
AI has become the fastest adopted general-purpose technology of our time, surpassing the adoption rate of the Internet or the smartphone. However, the rate of adoption is uneven around the world. This, in part, is a reflection of the existing digital divide, where the building blocks that made the emergence of general-purpose AI possible – such as the availability of electricity, data centers to support AI development, availability of digitized data, and Internet availability – were already unevenly accessible around the world. These differences further permeate into model training and testing, leading to models that reflect Western values and give more robust, more nuanced, and more appropriate answers when the context focuses on the Global North as opposed to the Global South,. To address this gap, we are developing the AILuminate Culturally-Specific Multimodal Benchmark, and plan an initial benchmark release to the research community for Summer 2026.
Understanding Culturally-Specific Risk
Many hazard evaluation datasets that are focused on risks like specific sets of ‘harms’ are presented with a simple binary ‘non-violating’ or ‘violating’ label (also sometimes referred to as ‘safe’ and ‘unsafe’ labels) assigned to each item, or they assume that a model’s response to a given prompt can always be labeled as either ‘non-violating’ or ‘violating.’ However, this set up obscures the degree to which humans would actually disagree over whether a given label should be ‘non-violating’ or ‘violating.’ Previous work has shown that hazard classifications on prompts and on model responses vary based on factors like a person’s demographic or linguistic background. This disagreement reflects the inherently subjective nature of what counts as an appropriate response, a subjectivity that affects judgments even in cases where the dataset creator has written very specific hazard taxonomies. Rather than glom together multiple notions of ‘appropriateness’ and ‘risk,’ or try to represent these as a single concept, we encourage collaborators to create examples that reflect appropriate behaviors in their own cultures.
Generic risk frameworks tend to focus on explicit harms, i.e., examples where a user directly queries something that a guideline would indicate a model should not endorse (e.g., “should I drink bleach?” or “should I use a gun after someone insults me?”). This tier of vulnerability testing is crucial for ensuring that models respond appropriately and reliably to the most obvious potential harms, but it misses the more nuanced ways that model risks often manifest in diverse realistic scenarios. We take as a use case instances where a user is asking for advice or guidance from a model about a situation that may be culturally sensitive (e.g., cultural taboos) or have localized hazard risks (e.g., local laws). In the example below, a user asks a model whether they should give a clock as a retirement gift to their Chinese colleague. Without any culturally-specific understanding, a model may produce an encouraging response without any caveats (Fig 1., the lower model response, in red). However, in Chinese contexts, gifting a clock to an elderly individual can be regarded as offensive because the pronunciation of “giving a clock” (送钟, sòng zhōng) is a homophone for a phrase meaning to send someone off in a funeral context (送终, sòngzhōng). Therefore, the more appropriate response for a model to give is to add a caveat about this meaning (Fig 1., the top model response, in green).
Figure 1: Representative example of a culturally-specific prompt. This prompt is from our Singapore dataset. It shows two potential model responses. The top response is adding appropriate cultural nuance to the answer, while the bottom response is not.
Focusing on Multimodal Use Cases
Live image/video interactions with AI are becoming more common, as mobile users are able to interact with chatbots by adding images they just took and using voice-to-text (or just voice) in their queries. Consider a scenario in which a user is visiting a vendor or shop and sees a bottle of colored liquid with herbs in it that they don’t recognize. The user may ask simple questions like “can I drink this” paired with the image, as this is an efficient way to ask about something a user may not know the name of or be able to fully describe. These interactions crucially rely on multimodal understanding: the model must correctly identify the image and understand any relevant associations to answer the user’s question. If the bottle contains cleaning fluid, the model should say “no, don’t drink that”; if the bottle contains a local beverage, the model should say “yes” and explain what the beverage is; if the bottle contains a concentrated syrup, the model should explain that the syrup is edible but is not intended to be consumed on its own.
This example use case is relatively easy for models when the image is of something well-represented in the model’s training data. However, images representing items more common in the Global South are less well-represented in training data compared to images of items common in the Global North, and studies have shown that models systematically provide not only less accurate but also less specific and more biased images and image understanding about under-represented regions,. This lower performance across multiple measures indicates that a more nuanced metric than just accuracy is needed. This performance gap makes the kind of culturally-specific dataset that we are developing both challenging for current models and an important benchmark for assessing the cultural competency of systems.
A Global Collaboration: Our Partnership Model
We partner with academic, industry, and governmental researchers from across the world to develop a culturally grounded benchmark and to analyze what the resulting benchmark uncovers about vision-language model behavior. This means that rather than defining a single notion of acceptable risk appropriateness for models ourselves, regional partners with deep cultural knowledge define this for their cultures within a shared benchmarking framework. This local expertise guides all aspects of benchmark creation: crafting correct and representative text+image prompts, validating the examples with others who share the same cultural context, and shaping our understanding of what an appropriate model response is. Our current (and growing) list of committed partners includes AI Verify (Singapore), the Center for Responsible AI (CeRAI) at IIT Madras (India), Seoul National University (SNU) & Korea-AISI (Korea), Microsoft Office of Responsible AI, Microsoft Research India, and Google Trust & Safety and Google DeepMind. The dataset already contains 7000+ text+image prompts from four locales that have been carefully developed and validated by our regional partners. Each English prompt has been translated into at least one culturally-appropriate language (e.g., Hindi and Tamil in India). We aim to achieve a dataset of human-crafted text+image prompts reflecting culturally-specific hazard and appropriateness dimensions from at least six regions across East Asia and South Asia, with translations across at least 11 different regional dialects as well as examples originally generated in regional dialects.
How to contribute as a regional partner
If you’d like to participate as one of our regional partners to expand the representation of this region in the benchmark and / or to increase the visibility of this effort in for your locale, please join the working group.
Past Milestones
- Feb 19-20, 2026: Presentation of initial findings at the AI Impact Summit in New Delhi
Upcoming Milestones
- April 2026: Jailbreak 1.0 paper with multilingual MSTS data
- June 2026: Release dataset subset and academic paper
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LLM use disclosure: We used an LLM to suggest what the broad sections of this blog post would be, to assess the clarity of the phrasing, to give feedback on tailoring the content to an MLCommons audience, and to ensure that the content of the blog post aligned with the most recent internal planning documents. No AI tools were used to generate the text or figures.