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Found 6 papers, 3 papers with code
Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations
Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting.
A Survey on Preserving Fairness Guarantees in Changing Environments
Human lives are increasingly being affected by the outcomes of automated decision-making systems and it is essential for the latter to be, not only accurate, but also fair.
Review of Mathematical frameworks for Fairness in Machine Learning
A review of the main fairness definitions and fair learning methodologies proposed in the literature over the last years is presented from a mathematical point of view.
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world.
Confidence Intervals for Testing Disparate Impact in Fair Learning
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning.
Obtaining fairness using optimal transport theory
\textit{Fairness} is generally studied in a probabilistic framework where it is assumed that there exists a protected variable, whose use as an input of the algorithm may imply discrimination.
Statistics Theory Statistics Theory 62H30, 68T01
