56 citations · 104 across the 6 of their papers we have counts for
7 papers
On the Impact of Multi-dimensional Local Differential Privacy on Fairness
Karima Makhlouf, Heber H. Arcolezi, Sami Zhioua +2
Automated decision systems are increasingly used to make consequential decisions in people's lives. Due to the sensitivity of the manipulated data as well as the resulting decision…
Shedding light on underrepresentation and Sampling Bias in machine learning
Sami Zhioua, Rūta Binkytė
Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplif…
Survey on Fairness Notions and Related Tensions
Guilherme Alves, Fabien Bernier, Miguel Couceiro +3
Automated decision systems are increasingly used to take consequential decisions in problems such as job hiring and loan granting with the hope of replacing subjective human decisi…
Causal Discovery for Fairness
Rūta Binkytė-Sadauskienė, Karima Makhlouf, Carlos Pinzón +2
It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular,…
Identifiability of Causal-based Fairness Notions: A State of the Art
Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
Machine learning algorithms can produce biased outcome/prediction, typically, against minorities and under-represented sub-populations. Therefore, fairness is emerging as an import…
Survey on Causal-based Machine Learning Fairness Notions
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 malt…