56 citations · 111 across the 8 of their papers we have counts for
6 papers · 1 filter
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
Karima Makhlouf, Tamara Stefanovic, Heber H. Arcolezi +1
Machine learning (ML) algorithms rely primarily on the availability of training data, and, depending on the domain, these data may include sensitive information about the data prov…
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…
(Local) Differential Privacy has NO Disparate Impact on Fairness
Héber H. Arcolezi, Karima Makhlouf, Catuscia Palamidessi
In recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in real-world applications. With LDP, users can perturb t…
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…
Machine learning fairness notions: Bridging the gap with real-world applications
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,…