97 research outputs found
Fair regression with wasserstein barycenters
We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We consider the case that the sensitive attribute is available for prediction. We establish a connection between fair regression and optimal transport theory, based on which we derive a close form expression for the optimal fair predictor. Specifically, we show that the distribution of this optimum is the Wasserstein barycenter of the distributions induced by the standard regression function on the sensitive groups. This result offers an intuitive interpretation of the optimal fair prediction and suggests a simple post-processing algorithm to achieve fairness. We establish risk and distribution-free fairness guarantees for this procedure. Numerical experiments indicate that our method is very effective in learning fair models, with a relative increase in error rate that is inferior to the relative gain in fairness
Fair Regression with Wasserstein Barycenters
International audienceWe study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We consider the case that the sensitive attribute is available for prediction. We establish a connection between fair regression and optimal transport theory, based on which we derive a close form expression for the optimal fair predictor. Specifically, we show that the distribution of this optimum is the Wasserstein barycenter of the distributions induced by the standard regression function on the sensitive groups. This result offers an intuitive interpretation of the optimal fair prediction and suggests a simple post-processing algorithm to achieve fairness. We establish risk and distribution-free fairness guarantees for this procedure. Numerical experiments indicate that our method is very effective in learning fair models, with a relative increase in error rate that is inferior to the relative gain in fairness
A Sequentially Fair Mechanism for Multiple Sensitive Attributes
In the standard use case of Algorithmic Fairness, the goal is to eliminate
the relationship between a sensitive variable and a corresponding score.
Throughout recent years, the scientific community has developed a host of
definitions and tools to solve this task, which work well in many practical
applications. However, the applicability and effectivity of these tools and
definitions becomes less straightfoward in the case of multiple sensitive
attributes. To tackle this issue, we propose a sequential framework, which
allows to progressively achieve fairness across a set of sensitive features. We
accomplish this by leveraging multi-marginal Wasserstein barycenters, which
extends the standard notion of Strong Demographic Parity to the case with
multiple sensitive characteristics. This method also provides a closed-form
solution for the optimal, sequentially fair predictor, permitting a clear
interpretation of inter-sensitive feature correlations. Our approach seamlessly
extends to approximate fairness, enveloping a framework accommodating the
trade-off between risk and unfairness. This extension permits a targeted
prioritization of fairness improvements for a specific attribute within a set
of sensitive attributes, allowing for a case specific adaptation. A data-driven
estimation procedure for the derived solution is developed, and comprehensive
numerical experiments are conducted on both synthetic and real datasets. Our
empirical findings decisively underscore the practical efficacy of our
post-processing approach in fostering fair decision-making
Mitigating Discrimination in Insurance with Wasserstein Barycenters
The insurance industry is heavily reliant on predictions of risks based on
characteristics of potential customers. Although the use of said models is
common, researchers have long pointed out that such practices perpetuate
discrimination based on sensitive features such as gender or race. Given that
such discrimination can often be attributed to historical data biases, an
elimination or at least mitigation is desirable. With the shift from more
traditional models to machine-learning based predictions, calls for greater
mitigation have grown anew, as simply excluding sensitive variables in the
pricing process can be shown to be ineffective. In this article, we first
investigate why predictions are a necessity within the industry and why
correcting biases is not as straightforward as simply identifying a sensitive
variable. We then propose to ease the biases through the use of Wasserstein
barycenters instead of simple scaling. To demonstrate the effects and
effectiveness of the approach we employ it on real data and discuss its
implications
Fairness in Multi-Task Learning via Wasserstein Barycenters
Algorithmic Fairness is an established field in machine learning that aims to
reduce biases in data. Recent advances have proposed various methods to ensure
fairness in a univariate environment, where the goal is to de-bias a single
task. However, extending fairness to a multi-task setting, where more than one
objective is optimised using a shared representation, remains underexplored. To
bridge this gap, we develop a method that extends the definition of
\textit{Strong Demographic Parity} to multi-task learning using multi-marginal
Wasserstein barycenters. Our approach provides a closed form solution for the
optimal fair multi-task predictor including both regression and binary
classification tasks. We develop a data-driven estimation procedure for the
solution and run numerical experiments on both synthetic and real datasets. The
empirical results highlight the practical value of our post-processing
methodology in promoting fair decision-making
Addressing Fairness and Explainability in Image Classification Using Optimal Transport
Algorithmic Fairness and the explainability of potentially unfair outcomes
are crucial for establishing trust and accountability of Artificial
Intelligence systems in domains such as healthcare and policing. Though
significant advances have been made in each of the fields separately, achieving
explainability in fairness applications remains challenging, particularly so in
domains where deep neural networks are used. At the same time, ethical
data-mining has become ever more relevant, as it has been shown countless times
that fairness-unaware algorithms result in biased outcomes. Current approaches
focus on mitigating biases in the outcomes of the model, but few attempts have
been made to try to explain \emph{why} a model is biased. To bridge this gap,
we propose a comprehensive approach that leverages optimal transport theory to
uncover the causes and implications of biased regions in images, which easily
extends to tabular data as well. Through the use of Wasserstein barycenters, we
obtain scores that are independent of a sensitive variable but keep their
marginal orderings. This step ensures predictive accuracy but also helps us to
recover the regions most associated with the generation of the biases. Our
findings hold significant implications for the development of trustworthy and
unbiased AI systems, fostering transparency, accountability, and fairness in
critical decision-making scenarios across diverse domains
Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding
In this work, we investigate Gaussian Processes indexed by multidimensional
distributions. While directly constructing radial positive definite kernels
based on the Wasserstein distance has been proven to be possible in the
unidimensional case, such constructions do not extend well to the
multidimensional case as we illustrate here. To tackle the problem of defining
positive definite kernels between multivariate distributions based on optimal
transport, we appeal instead to Hilbert space embeddings relying on optimal
transport maps to a reference distribution, that we suggest to take as a
Wasserstein barycenter. We characterize in turn radial positive definite
kernels on Hilbert spaces, and show that the covariance parameters of virtually
all parametric families of covariance functions are microergodic in the case of
(infinite-dimensional) Hilbert spaces. We also investigate statistical
properties of our suggested positive definite kernels on multidimensional
distributions, with a focus on consistency when a population Wasserstein
barycenter is replaced by an empirical barycenter and additional explicit
results in the special case of Gaussian distributions. Finally, we study the
Gaussian process methodology based on our suggested positive definite kernels
in regression problems with multidimensional distribution inputs, on simulation
data stemming both from synthetic examples and from a mechanical engineering
test case
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