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of 43
pro vyhledávání: '"Krasanakis, Emmanouil"'
Artificial intelligence systems often address fairness concerns by evaluating and mitigating measures of group discrimination, for example that indicate biases against certain genders or races. However, what constitutes group fairness depends on who
Externí odkaz:
http://arxiv.org/abs/2406.18939
Creating fair AI systems is a complex problem that involves the assessment of context-dependent bias concerns. Existing research and programming libraries express specific concerns as measures of bias that they aim to constrain or mitigate. In practi
Externí odkaz:
http://arxiv.org/abs/2405.19022
Graph filters that transform prior node values to posterior scores via edge propagation often support graph mining tasks affecting humans, such as recommendation and ranking. Thus, it is important to make them fair in terms of satisfying statistical
Externí odkaz:
http://arxiv.org/abs/2303.08157
Decentralization is emerging as a key feature of the future Internet. However, effective algorithms for search are missing from state-of-the-art decentralized technologies, such as distributed hash tables and blockchain. This is surprising, since dec
Externí odkaz:
http://arxiv.org/abs/2204.12902
In this work, we aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. Graph Neural Networks (GNNs) are known to improve the accuracy of simple classifiers in centr
Externí odkaz:
http://arxiv.org/abs/2111.14837
We introduce pygrank, an open source Python package to define, run and evaluate node ranking algorithms. We provide object-oriented and extensively unit-tested algorithm components, such as graph filters, post-processors, measures, benchmarks and onl
Externí odkaz:
http://arxiv.org/abs/2110.09274
Graph filters are an emerging paradigm that systematizes information propagation in graphs as transformation of prior node values, called graph signals, to posterior scores. In this work, we study the problem of mitigating disparate impact, i.e. post
Externí odkaz:
http://arxiv.org/abs/2108.12397
Publikováno v:
In Information and Software Technology July 2024 171
Autor:
Ntoutsi, Eirini, Fafalios, Pavlos, Gadiraju, Ujwal, Iosifidis, Vasileios, Nejdl, Wolfgang, Vidal, Maria-Esther, Ruggieri, Salvatore, Turini, Franco, Papadopoulos, Symeon, Krasanakis, Emmanouil, Kompatsiaris, Ioannis, Kinder-Kurlanda, Katharina, Wagner, Claudia, Karimi, Fariba, Fernandez, Miriam, Alani, Harith, Berendt, Bettina, Kruegel, Tina, Heinze, Christian, Broelemann, Klaus, Kasneci, Gjergji, Tiropanis, Thanassis, Staab, Steffen
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefor
Externí odkaz:
http://arxiv.org/abs/2001.09762
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