This studio was about taking human decisions and solving them using computer friendly algorithms. This was often counter-intuitive, making simple to solve problems into processes with several steps and variables. We wanted to make them thorough so they considered all factors, and we wanted to prompt the user for info with a simple and user-friendly website.
The issue we’ve tried to address is that it is very difficult to find restaurants to eat at when one has multiple or severe allergies. We decided to create a website that takes in a list of allergens to avoid, as well as cuisine types that the user prefers, and returns a list of restaurants in order of most to least likely to work. To do this it takes into account ingredients lists for individual dishes, as well as kitchen contaminations for more severe allergies. It also considers type of food and Yelp rating.
We designed a simple input system for restaurants that could be sent to them en masse, allowing us to uptake and update our restaurant data more effectively than alternatives like AllergyEats that rely on users submitting data. This strategy of 100% user-submitted information works well for major chains like McDonalds, but fails to give users information for smaller restaurants. We didn’t have time to code this restaurant-facing interface, but we wrote a proof-of-concept for the restaurant ranking which is entirely functional. We also wanted to add options for different types of food, selectable with checkboxes. This would make our algorithm more complex, but we’ve included it in our mockups.