9 citations · 13 across the 4 of their papers we have counts for
4 papers
The Misuse of AUC: What High Impact Risk Assessment Gets Wrong
Kweku Kwegyir-Aggrey, Marissa Gerchick, Malika Mohan +2
When determining which machine learning model best performs some high impact risk assessment task, practitioners commonly use the Area under the Curve (AUC) to defend and validate…
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai +3
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical dist…
Model Selection's Disparate Impact in Real-World Deep Learning Applications
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey +2
Algorithmic fairness has emphasized the role of biased data in automated decision outcomes. Recently, there has been a shift in attention to sources of bias that implicate fairness…
Everything is Relative: Understanding Fairness with Optimal Transport
Kweku Kwegyir-Aggrey, Rebecca Santorella, Sarah M. Brown
To study discrimination in automated decision-making systems, scholars have proposed several definitions of fairness, each expressing a different fair ideal. These definitions requ…