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Future-Proofing AI

Web-tool that helps stakeholders learn to create a machine learning model
Role: Researcher and Designer
Team: Angie Zhang, Olympia Walker, Ben Chen, Janet Dai, Dr. Min Lee
Duration:
September 6 - December 17, 2021
Brief
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. 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. We create a web-tool that helps stakeholders learn to create a machine learning model so that they may analyze historical data and their model results for past decision-making patterns.
Status
Undergoing review.
Due to the dataset containing sensitive and confidential information used in this research study, I’m unable to include detailed images of the final tool design.
For more details on this project, reach out to kacinguyen[at]utexas.edu.


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