arXiv subjects

Paula Gordaliza

Publications and source records attributed to Paula Gordaliza.

12 recordsLinked to original sources

arXiv2025

A proposal of smooth interpolation to optimal transport for restoring biased data for algorithmic fairness

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, come into play. Frequently, the problem is not only in the algorithm itself, but also in the biased data feeding the algorithm, which is just the reflection of the societal bias. Thus, this `food' given to the algorithm have to be repaired in order to produce unbiased results for all. As a simple, but frequent case, two different subgroups will be considered, the privileged and the unprivileged groups. Assuming that results should not depend on such characteristic dividing the data, the rest of attributes in each group have to be moved (transported) so that its underlying distribution can be considered similar. For doing this, optimal transport (OT) theory is used to effectively transport the values of the features, excluding the sensitive variable, to the so-called {\it Wasserstein barycenter} of the two distributions conditional to each group. An efficient procedure based on the {\it auction algorithm} is adapted for doing so. The transportation is made for the data at hand. If new data arrive then the OT problem has to be solved for the new set gathering previous and incoming data, which is rather inefficient. Alternatively, an implementation of a smooth interpolation procedure called \textit{Extended Total Repair (ExTR)} is proposed, which is one of the main contributions of the paper. The methodology is applied with satisfactory results to simulated biased data as well as to a real case for a German credit dataset used for risk assessment prediction.

stat.AP↗

arXiv2025

PLS-based approach for fair representation learning

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 dimension of the data by providing representation tailored for the prediction. We propose a novel method to incorporate fairness constraints in the construction of PLS components. This new algorithm provides a feasible way to construct such features both in the linear and the non linear case using kernel embeddings. The efficiency of our method is evaluated on different datasets, and we prove its superiority with respect to standard fair PCA method.

cs.LG↗

arXiv2024

Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers

This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks.

cs.LG↗

arXiv2023

A study on group fairness in healthcare outcomes for nursing home residents during the COVID-19 pandemic in the Basque Country

We explore the effect of nursing home status on healthcare outcomes such as hospitalisation, mortality and in-hospital mortality during the COVID-19 pandemic. Some claim that in specific Autonomous Communities (geopolitical divisions) in Spain, elderly people in nursing homes had restrictions on access to hospitals and treatments, which raised a public outcry about the fairness of such measures. In this work, the case of the Basque Country is studied under a rigorous statistical approach and a physician's perspective. As fairness/unfairness is hard to model mathematically and has strong real-world implications, this work concentrates on the following simplification: establishing if the nursing home status had a direct effect on healthcare outcomes once accounted for other meaningful patients' information such as age, health status and period of the pandemic, among others. The methods followed here are a combination of established techniques as well as new proposals from the fields of causality and fair learning. The current analysis suggests that as a group, people in nursing homes were significantly less likely to be hospitalised, and considerably more likely to die, even in hospitals, compared to their non-residents counterparts during most of the pandemic. Further data collection and analysis are needed to guarantee that this is solely/mainly due to nursing home status.

stat.ME↗

arXiv2023

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. We propose the Uncertainty Matters (UM) framework that generalizes a Beta-Binomial approach to derive the posterior distribution of any criteria combination, allowing stable performance assessment in a bias-aware setting.We suggest modeling the confusion matrix of each demographic group using a Multinomial distribution updated through a Bayesian procedure. We extend UM to be applicable under the popular K-fold cross-validation procedure. Experiments highlight the benefits of UM over classical evaluation frameworks regarding informativeness and stability.

cs.LG↗

arXiv2022

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. The literature of algorithmic fairness has grown considerably over the last decade, where most of the approaches are evaluated under the strong assumption that the train and test samples are independently and identically drawn from the same underlying distribution. However, in practice, dissimilarity between the training and deployment environments exists, which compromises the performance of the decision-making algorithm as well as its fairness guarantees in the deployment data. There is an emergent research line that studies how to preserve fairness guarantees when the data generating processes differ between the source (train) and target (test) domains, which is growing remarkably. With this survey, we aim to provide a wide and unifying overview on the topic. For such purpose, we propose a taxonomy of the existing approaches for fair classification under distribution shift, highlight benchmarking alternatives, point out the relation with other similar research fields and eventually, identify future venues of research.

cs.LG↗

arXiv2020

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. Following our independence-based approach, we consider how to build fair algorithms and the consequences on the degradation of their performance compared to the possibly unfair case. This corresponds to the price for fairness given by the criteria $\textit{statistical parity}$ or $\textit{equality of odds}$. Novel results giving the expressions of the optimal fair classifier and the optimal fair predictor (under a linear regression gaussian model) in the sense of $\textit{equality of odds}$ are presented.

stat.ML↗

arXiv2020

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. A critical question then recently arised among the population: Do algorithmic decisions convey any type of discrimination against specific groups of population or minorities? In this paper, we show the importance of understanding how a bias can be introduced into automatic decisions. We first present a mathematical framework for the fair learning problem, specifically in the binary classification setting. We then propose to quantify the presence of bias by using the standard Disparate Impact index on the real and well-known Adult income data set. Finally, we check the performance of different approaches aiming to reduce the bias in binary classification outcomes. Importantly, we show that some intuitive methods are ineffective. This sheds light on the fact trying to make fair machine learning models may be a particularly challenging task, in particular when the training observations contain a bias.

stat.ML↗

arXiv2018

Central limit theorem and bootstrap procedure for Wasserstein's variations with an application to structural relationships between distributions

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 structural relationships between the observations. We propose the estimation of the quantiles of the empirical process of Wasserstein's variation using a bootstrap procedure. We then use these results for statistical inference on a distribution registration model for general deformation functions. The tests are based on the variance of the distributions with respect to their Wasserstein's barycenters for which we prove central limit theorems, including bootstrap versions.

math.ST↗

arXiv2018

Obtaining fairness using optimal transport theory

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 a particular group of people, typically a minority? \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. There are different definitions of Fairness in the literature. In this paper we focus on two of them which are called Disparate Impact (DI) and Balanced Error Rate (BER). Both are based on the outcome of the algorithm across the different groups determined by the protected variable. The relationship between these two notions is also studied. The goals of this paper are to detect when a binary classification rule lacks fairness and to try to fight against the potential discrimination attributable to it. This can be done by modifying either the classifiers or the data itself. Our work falls into the second category and modifies the input data using optimal transport theory.

math.ST↗

arXiv2018

A Central Limit Theorem for $L_p$ transportation cost with applications to Fairness Assessment in Machine Learning

We provide a Central Limit Theorem for the Monge-Kantorovich distance between two empirical distributions with size $n$ and $m$, $W_p(P_n,Q_m)$ for $p>1$ for observations on the real line, using a minimal amount of assumptions. We provide an estimate of the asymptotic variance which enables to build a two sample test to assess the similarity between two distributions. This test is then used to provide a new criterion to assess the notion of fairness of a classification algorithm.

math.ST↗

arXiv2018

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. We aim at promoting the use of confidence intervals when testing the so-called group disparate impact. We illustrate on some examples the importance of using confidence intervals and not a single value.

stat.ML↗