51 citations · 86 across the 6 of their papers we have counts for
8 papers
A proposal of smooth interpolation to optimal transport for restoring biased data for algorithmic fairness
Elena M. De Diego, Paula Gordaliza, Jesús Lopez-Fidalgo
The so-called algorithmic bias is a hot topic in the decision making process based on Artificial Intelligence, especially when demographics, such as gender, age or ethnic origin, c…
PLS-based approach for fair representation learning
Elena M. De-Diego, Adrián Perez-Suay, Paula Gordaliza +1
We revisit the problem of fair representation learning by proposing Fair Partial Least Squares (PLS) components. PLS is widely used in statistics to efficiently reduce the dimensio…
Review of Mathematical frameworks for Fairness in Machine Learning
Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
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. Following o…
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set
Philippe Besse, Eustasio del Barrio, Paula Gordaliza +2
Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world. A critical question then recently arised among…
A Central Limit Theorem for transportation cost with applications to Fairness Assessment in Machine Learning
Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
We provide a Central Limit Theorem for the Monge-Kantorovich distance between two empirical distributions with size and , for for observations on the re…
Obtaining fairness using optimal transport theory
Eustasio del Barrio, Fabrice Gamboa, Paula Gordaliza +1
Statistical algorithms are usually helping in making decisions in many aspects of our lives. But, how do we know if these algorithms are biased and commit unfair discrimination of…
Confidence Intervals for Testing Disparate Impact in Fair Learning
Philippe Besse, Eustasio del Barrio, Paula Gordaliza +1
We provide the asymptotic distribution of the major indexes used in the statistical literature to quantify disparate treatment in machine learning. We aim at promoting the use of c…
Central limit theorem and bootstrap procedure for Wasserstein's variations with an application to structural relationships between distributions
Eustasio Del Barrio, Paula Gordaliza, Hélène Lescornel +1
Wasserstein barycenters and variance-like criteria based on the Wasserstein distance are used in many problems to analyze the homogeneity of collections of distributions and struct…