Auditing the Fairness of COVID-19 Forecast Hub Case Prediction Models

no code implementations • 17 May 2024 • Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Vanessa Frias-Martinez

The COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications.

Decision Making Fairness

DemOpts: Fairness corrections in COVID-19 case prediction models

no code implementations • 15 May 2024 • Naman Awasthi, Saad Abrar, Daniel Smolyak, Vanessa Frias-Martinez

Nevertheless, related work has revealed under-reporting bias in COVID-19 cases as well as sampling bias in mobility data for certain minority racial and ethnic groups, which could in turn affect the fairness of the COVID-19 predictions along race labels.

Decision Making Fairness

Developing an Effective and Automated Patient Engagement Estimator for Telehealth: A Machine Learning Approach

no code implementations • 17 Nov 2020 • Pooja Guhan, Naman Awasthi, and Kathryn McDonald, Kristin Bussell, Dinesh Manocha, Gloria Reeves, Aniket Bera

We discuss MET, a learning-based algorithm proposed for perceiving a patient's level of engagement during telehealth sessions.

regression