An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

no code implementations • 29 Sep 2021 • Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney, Reginald E. Bryant

To ensure trust in AI models, it is becoming increasingly apparent that evaluation of models must be extended beyond traditional performance metrics, like accuracy, to other dimensions, such as fairness, explainability, adversarial robustness, and distribution shift.

Adversarial Robustness Fairness

Preservation of Anomalous Subgroups On Machine Learning Transformed Data

no code implementations • 9 Nov 2019 • Samuel C. Maina, Reginald E. Bryant, William O. Goal, Robert-Florian Samoilescu, Kush R. Varshney, Komminist Weldemariam

Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset.

BIG-bench Machine Learning Subgroup Discovery