World Wide Dishes

We present the World Wide Dishes dataset which seeks to assess disparities in representations of food through a decentralised data collection effort to gather perspectives directly from people with a wide variety of backgrounds from around the globe with the aim of creating a dataset consisting of their insights into their own experiences of foods relevant to their cultural, regional, national, or ethnic lives.

The data that we curated include the name of the dish (both in the local language and in English), the country of origin, the region of origin, the associated culture, the time of day at which the meal is eaten, the type of meal, the utensils used, the drinks that accompany the meal, any special occasions when the meal is eaten, the ingredients, the recipe, and the image of the dish if available.

We then used this curated list of dishes with its labels to assess the current AI systems' ability to understand the diversity of food cultures. We tested both Large Language Models (GPT 3.5, Llama 3 - 8B model, Llama 3 - 70B model) and image generation models (DALL-E 2, DALL-E 3, Stable Diffusion v2.1) to see if there are any biases in the models' capabilities.