17 citations · 19 across the 3 of their papers we have counts for
3 papers
General Post-Processing Framework for Fairness Adjustment of Machine Learning Models
Léandre Eberhard, Nirek Sharma, Filipp Shelobolin +1
As machine learning increasingly influences critical domains such as credit underwriting, public policy, and talent acquisition, ensuring compliance with fairness constraints is bo…
FairCanary: Rapid Continuous Explainable Fairness
Avijit Ghosh, Aalok Shanbhag, Christo Wilson
Systems that offer continuous model monitoring have emerged in response to (1) well-documented failures of deployed Machine Learning (ML) and Artificial Intelligence (AI) models an…
Unified Shapley Framework to Explain Prediction Drift
Aalok Shanbhag, Avijit Ghosh, Josh Rubin
Predictions are the currency of a machine learning model, and to understand the model's behavior over segments of a dataset, or over time, is an important problem in machine learni…