18 citations · 34 across the 10 of their papers we have counts for
13 papers
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…
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…
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…
Towards Mobility Data Science (Vision Paper)
Mohamed Mokbel, Mahmoud Sakr, Li Xiong +45
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracki…
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…
Ranking Papers by their Short-Term Scientific Impact
Ilias Kanellos, Thanasis Vergoulis, Dimitris Sacharidis +2
The constantly increasing rate at which scientific papers are published makes it difficult for researchers to identify papers that currently impact the research field of their inte…
Finding Desirable Objects under Group Categorical Preferences
Nikos Bikakis, Karim Benouaret, Dimitris Sacharidis
Considering a group of users, each specifying individual preferences over categorical attributes, the problem of determining a set of objects that are objectively preferable by all…
Routing Directions: Keeping it Fast and Simple
Dimitris Sacharidis, Panagiotis Bouros
The problem of providing meaningful routing directions over road networks is of great importance. In many real-life cases, the fastest route may not be the ideal choice for providi…
Diachronic Linked Data: Towards Long-Term Preservation of Structured Interrelated Information
Yannis Stavrakas, George Papastefanatos, Theodore Dalamagas +1
The Linked Data Paradigm is one of the most promising technologies for publishing, sharing, and connecting data on the Web, and offers a new way for data integration and interopera…