3 citations · 8 across the 11 of their papers we have counts for
4 papers · 1 filter
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
Loukas Kavouras, Eleni Psaroudaki, Konstantinos Tsopelas +9
The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods…
FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups
Giorgos Giannopoulos, Dimitris Sacharidis, Nikolas Theologitis +2
Fairness is steadily becoming a crucial requirement of Machine Learning (ML) systems. A particularly important notion is subgroup fairness, i.e., fairness in subgroups of individua…
Fairness Aware Counterfactuals for Subgroups
Loukas Kavouras, Konstantinos Tsopelas, Giorgos Giannopoulos +6
In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisit…
Auditing for Spatial Fairness
Dimitris Sacharidis, Giorgos Giannopoulos, George Papastefanatos +1
This paper studies algorithmic fairness when the protected attribute is location. To handle protected attributes that are continuous, such as age or income, the standard approach i…