EarshotUser Research, Service Design,
Product Design
2020 - 2021
In the current system of aid, relief is distributed based on hypotheses and old knowledge. While most organizations collect data about disasters, they rarely collect data from people experiencing disasters directly. Earshot democratizes information for both those impacted by a natural disaster, and for the organizations and governments that serve them. At the center of the solution is an AI-driven chatbot named Quinn.
Earshot’s Quinn uses a robust database that is built by text messages and phone calls from previous survivors to answer questions: How do I apply for a grant to repair my home? Which pharmacies nearby have the prescription I need? Does the local grocery store have bread? Earshot uses machine learning to convert this database into actionable insights for governments and other organizations to deliver better, faster, and more accurate aid.
This project won first place at the 2020 Rotman Design Challenge.
As most of my projects have strict confidentiality rules, what is shown here is only a glimpse of my work.
