2 citations · 4 across the 3 of their papers we have counts for
4 papers
Fairmetrics: An R package for group fairness evaluation
Benjamin Smith, Jianhui Gao, Jessica Gronsbell
Fairness is a growing area of machine learning (ML) that focuses on ensuring models do not produce systematically biased outcomes for specific groups, particularly those defined by…
Reliable fairness auditing with semi-supervised inference
Jianhui Gao, Jessica Gronsbell
Machine learning (ML) models often exhibit bias that can exacerbate inequities in biomedical applications. Fairness auditing, the process of evaluating a model's performance across…
Another look at statistical inference with machine learning-imputed data
Jessica Gronsbell, Jianhui Gao, Zachary R. McCaw +2
From structural biology to epidemiology, predictions from machine learning (ML) models increasingly complement costly gold-standard data, enabling faster, more affordable, and scal…
What is Fair? Defining Fairness in Machine Learning for Health
Jianhui Gao, Benson Chou, Zachary R. McCaw +4
Ensuring that machine learning (ML) models are safe, effective, and equitable across all patients is critical for clinical decision-making and for preventing the amplification of e…