2 research outputs found
A multi-objective-based approach for Fair Principal Component Analysis
In dimension reduction problems, the adopted technique may produce
disparities between the representation errors of two or more different groups.
For instance, in the projected space, a specific class can be better
represented in comparison with the other ones. Depending on the situation, this
unfair result may introduce ethical concerns. In this context, this paper
investigates how a fairness measure can be considered when performing dimension
reduction through principal component analysis. Since both reconstruction error
and fairness measure must be taken into account, we propose a
multi-objective-based approach to tackle the Fair Principal Component Analysis
problem. The experiments attest that a fairer result can be achieved with a
very small loss in the reconstruction error
Debiasing Machine Learning Models by Using Weakly Supervised Learning
We tackle the problem of bias mitigation of algorithmic decisions in a
setting where both the output of the algorithm and the sensitive variable are
continuous. Most of prior work deals with discrete sensitive variables, meaning
that the biases are measured for subgroups of persons defined by a label,
leaving out important algorithmic bias cases, where the sensitive variable is
continuous. Typical examples are unfair decisions made with respect to the age
or the financial status. In our work, we then propose a bias mitigation
strategy for continuous sensitive variables, based on the notion of endogeneity
which comes from the field of econometrics. In addition to solve this new
problem, our bias mitigation strategy is a weakly supervised learning method
which requires that a small portion of the data can be measured in a fair
manner. It is model agnostic, in the sense that it does not make any hypothesis
on the prediction model. It also makes use of a reasonably large amount of
input observations and their corresponding predictions. Only a small fraction
of the true output predictions should be known. This therefore limits the need
for expert interventions. Results obtained on synthetic data show the
effectiveness of our approach for examples as close as possible to real-life
applications in econometrics.Comment: 30 pages, 25 figure