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Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index
We further discuss multiple aspects of model monitoring and robustness that need to be analyzed \emph{simultaneously} to achieve robustness for industry safety standards.
Commuting Network Spillovers and COVID-19 Deaths Across US Counties
This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19.
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution
We aim to address this problem in settings where the predictive model is a black box; That is, we can only observe the response of the model to various inputs, but have no knowledge about the internal structure of the predictive model, its parameters, the objective function, and the algorithm used to optimize the model.
Algorithmic Bias in Recidivism Prediction: A Causal Perspective
Specifically, we assess whether COMPAS exhibits racial bias against African American defendants using FACT, a recently introduced causality grounded measure of algorithmic fairness.
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc.
