Future-Proofing AI

Web-tool that helps stakeholders learn to create a machine learning model
Researcher and Designer
Angie Zhang, Olympia Walker, Ben Chen, Janet Dai, Dr. Min Lee
Sep. 6 - Dec. 17, 2021
React and Material UI (frontend), Flask and MongoDB (backend), plotly.js


We address the potential of a participatory algorithm design that incorporates stakeholder deliberation and can be used as a technique for “future-proofing”—surfacing past and present human biases so as to improve future decision-making processes, whether algorithmic or human.


Past work exploring algorithmic fairness has focused on how to define fairness and how to create fairer algorithms. These efforts include designing participatory algorithms or auditing algorithms for disparate outcomes. However, participatory algorithm designs have yet to explore integrating stakeholder deliberation, and algorithmic auditing may be limited as a reactive approach.


Help stakeholders learn to create a machine-learning model so that they may analyze historical data and their model results for past decision-making patterns



Coming soon! 🎉 This paper has recently been accepted to the 26th ACM Conference On Computer-Supported Cooperative Work And Social Computing.
Future-Proofing AI