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pro vyhledávání: '"Makhlouf, Karima"'
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 providers, thus leading to significant privacy issues. Differential privac
Externí odkaz:
http://arxiv.org/abs/2405.14725
Autor:
Makhlouf, Karima, Arcolezi, Heber H., Zhioua, Sami, Brahim, Ghassen Ben, Palamidessi, Catuscia
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 decisions, several ethical concerns need to be addressed for the appropriate u
Externí odkaz:
http://arxiv.org/abs/2312.04404
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 their data on their devices before sending it out for analysis. However
Externí odkaz:
http://arxiv.org/abs/2304.12845
Autor:
Alves, Guilherme, Bernier, Fabien, Couceiro, Miguel, Makhlouf, Karima, Palamidessi, Catuscia, Zhioua, Sami
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 decisions with objective machine learning (ML) algorithms. However, ML-based
Externí odkaz:
http://arxiv.org/abs/2209.13012
Autor:
Binkytė-Sadauskienė, Rūta, Makhlouf, Karima, Pinzón, Carlos, Zhioua, Sami, Palamidessi, Catuscia
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, guarantees that the ML decisions do not result in discrimination again
Externí odkaz:
http://arxiv.org/abs/2206.06685
Machine learning algorithms can produce biased outcome/prediction, typically, against minorities and under-represented sub-populations. Therefore, fairness is emerging as an important requirement for the large scale application of machine learning ba
Externí odkaz:
http://arxiv.org/abs/2203.05900
Publikováno v:
In Journal of Logical and Algebraic Methods in Programming October 2024 141
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. Several notions of fa
Externí odkaz:
http://arxiv.org/abs/2010.09553
Publikováno v:
Information Processing and Management, 58(5). pp. 107-132 (2021)
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. Given the inherent subjectivity of viewing
Externí odkaz:
http://arxiv.org/abs/2006.16745
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