6 papers
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…
Source Attribution in Retrieval-Augmented Generation
Ikhtiyor Nematov, Tarik Kalai, Elizaveta Kuzmenko +4
While attribution methods, such as Shapley values, are widely used to explain the importance of features or training data in traditional machine learning, their application to Larg…
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. Existing approaches lack customi…
The Susceptibility of Example-Based Explainability Methods to Class Outliers
Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi +1
This study explores the impact of class outliers on the effectiveness of example-based explainability methods for black-box machine learning models. We reformulate existing explain…
Fairness in AI: challenges in bridging the gap between algorithms and law
Giorgos Giannopoulos, Maria Psalla, Loukas Kavouras +4
In this paper we examine algorithmic fairness from the perspective of law aiming to identify best practices and strategies for the specification and adoption of fairness definition…
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…