26 citations · 86 across the 28 of their papers we have counts for
7 papers · 1 filter
FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning
Arjun Roy, Christos Koutlis, Symeon Papadopoulos +1
The generalisation capacity of Multi-Task Learning (MTL) suffers when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients. This is…
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Vasilis Gkolemis, Anargiros Tzerefos, Theodore Dalamagas +2
Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate funct…
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi +1
Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated feature…
Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy
Siamak Ghodsi, Eirini Ntoutsi
Group imbalance, resulting from inadequate or unrepresentative data collection methods, is a primary cause of representation bias in datasets. Representation bias can exist with re…
Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview
Arjun Roy, Jan Horstmann, Eirini Ntoutsi
AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The do…
Explaining text classifiers through progressive neighborhood approximation with realistic samples
Yi Cai, Arthur Zimek, Eirini Ntoutsi +1
The importance of neighborhood construction in local explanation methods has been already highlighted in the literature. And several attempts have been made to improve neighborhood…