Techniques for reducing bias and ensuring fairness and equity. AI methods and project examples.
There is an urgent need for disciplined, automated, data-driven approaches for coordinating the allocation of scarce resources (healthcare workers, PPE, ventilators).
In this project, we develop novel data-driven optimization methodologies that produce accurate, personalized wait time estimates for individual candidates.
We want to learn the priorities of stakeholders for a given decision-making problem in order to create more fair, accountable, and transparent decisions.
Motivated from the problems faced by underserved communities or in underresourced settings, we are working to define and quantify fairness in machine learning and resource allocation.
To develop a fair and efficient algorithm which creates personalized matches for persons/families experiencing homelessness.