Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations

no code implementations • 8 May 2022 • Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi

In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes.

Downstream Fairness Caveats with Synthetic Healthcare Data

no code implementations • 9 Mar 2022 • Karan Bhanot, Ioana Baldini, Dennis Wei, Jiaming Zeng, Kristin P. Bennett

In this paper, we evaluate the fairness of models generated on two healthcare datasets for gender and race biases.

Fairness Generative Adversarial Network

The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

no code implementations • 29 Nov 2018 • Jiaming Zeng, Adam Lesnikowski, Jose M. Alvarez

One of the main challenges of deep learning tools is their inability to capture model uncertainty.

Active Learning Deep Learning +1

Interpretable Classification Models for Recidivism Prediction

no code implementations • 26 Mar 2015 • Jiaming Zeng, Berk Ustun, Cynthia Rudin

We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making.

BIG-bench Machine Learning Classification +2