1 code implementation • 7 Dec 2023 • Karima Makhlouf, Heber H. Arcolezi, Sami Zhioua, Ghassen Ben Brahim, Catuscia Palamidessi
Automated decision systems are increasingly used to make consequential decisions in people's lives.
1 code implementation • 25 Apr 2023 • Héber H. Arcolezi, Karima Makhlouf, Catuscia Palamidessi
However, as the collection of multiple sensitive information becomes more prevalent across various industries, collecting a single sensitive attribute under LDP may not be sufficient.
no code implementations • 16 Sep 2022 • Guilherme Alves, Fabien Bernier, Miguel Couceiro, Karima Makhlouf, Catuscia Palamidessi, Sami Zhioua
Fairness requirements to be satisfied while learning models created several types of tensions among the different notions of fairness and other desirable properties such as privacy and classification accuracy.
no code implementations • 14 Jun 2022 • Rūta Binkytė-Sadauskienė, Karima Makhlouf, Carlos Pinzón, Sami Zhioua, Catuscia Palamidessi
Existing causal approaches to fairness in the literature do not address this problem and assume that the causal model is available.
no code implementations • 11 Mar 2022 • Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
This paper is a compilation of the major identifiability results which are of particular relevance for machine learning fairness.
no code implementations • 19 Oct 2020 • Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc.
no code implementations • 30 Jun 2020 • Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
Fairness emerged as an important requirement to guarantee that Machine Learning (ML) predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities.