5 papers
Geometrical fairness in graph neural networks
Arturo Pérez-Peralta, Sandra BenÃtez-Peña, Blas Kolic +1
Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications. Among these, recent frameworks grounded in diffusion pr…
Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
Sandra BenÃtez-Peña, Blas Kolic, Victoria Menendez +1
Algorithmic fairness has become a central concern in computational decision-making systems, where ensuring equitable outcomes is essential for both ethical and legal reasons. Two d…
On the use of graph models to achieve individual and group fairness
Arturo Pérez-Peralta, Sandra BenÃtez-Peña, Rosa E. Lillo
Machine Learning algorithms are ubiquitous in key decision-making contexts such as justice, healthcare and finance, which has spawned a great demand for fairness in these procedure…
FairLangProc: A Python package for fairness in NLP
Arturo Pérez-Peralta, Sandra BenÃtez-Peña, Rosa E. Lillo
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizat…
The more the merrier: logical and multistage processors in credit scoring
Arturo Pérez-Peralta, Sandra BenÃtez-Peña, Rosa E. Lillo
Machine Learning algorithms are ubiquitous in key decision-making contexts such as organizational justice or healthcare, which has spawned a great demand for fairness in these proc…