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Found 6 papers, 1 papers with code
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
For instance, if commonly used criteria, such as independence or sufficiency, are satisfied for a prediction score $S$ used for binary classification, they need not be satisfied after an application of a simple thresholding operation on $S$ (as commonly used in practice).
Fairness-Accuracy Trade-Offs: A Causal Perspective
Thus, we introduce a quantity that summarizes the fairness-utility trade-off, called the causal fairness/utility ratio, defined as the ratio of the reduction in discrimination vs. the excess loss from constraining a causal pathway.
Fair Clustering: A Causal Perspective
Our approach enables the specification of the causal fairness metrics that should be minimised.
Reconciling Predictive and Statistical Parity: A Causal Approach
Since the rise of fair machine learning as a critical field of inquiry, many different notions on how to quantify and measure discrimination have been proposed in the literature.
Causal Fairness Analysis
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms that generate the disparity in the first place, challenge we call the Fundamental Problem of Causal Fairness Analysis (FPCFA).
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.
