Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Sacharidis, Dimitris"'
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 explainability evaluation metrics, such as correctness and relevance, specifi
Externí odkaz:
http://arxiv.org/abs/2407.20678
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 customization for user intent and often provide a homogeneous set of explanat
Externí odkaz:
http://arxiv.org/abs/2407.16010
Autor:
Kavouras, Loukas, Psaroudaki, Eleni, Tsopelas, Konstantinos, Rontogiannis, Dimitrios, Theologitis, Nikolaos, Sacharidis, Dimitris, Giannopoulos, Giorgos, Tomaras, Dimitrios, Markou, Kleopatra, Gunopulos, Dimitrios, Fotakis, Dimitris, Emiris, Ioannis
The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expresse
Externí odkaz:
http://arxiv.org/abs/2405.18921
Autor:
Giannopoulos, Giorgos, Psalla, Maria, Kavouras, Loukas, Sacharidis, Dimitris, Marecek, Jakub, Matilla, German M, Emiris, Ioannis
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 definitions and algorithms in real-world systems and use cases. We start by prov
Externí odkaz:
http://arxiv.org/abs/2404.19371
Autor:
Giannopoulos, Giorgos, Sacharidis, Dimitris, Theologitis, Nikolas, Kavouras, Loukas, Emiris, Ioannis
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 individuals that are defined by more than one attributes. Identifying bias in s
Externí odkaz:
http://arxiv.org/abs/2404.18685
Autor:
Kavouras, Loukas, Tsopelas, Konstantinos, Giannopoulos, Giorgos, Sacharidis, Dimitris, Psaroudaki, Eleni, Theologitis, Nikolaos, Rontogiannis, Dimitrios, Fotakis, Dimitris, Emiris, Ioannis
In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisiting (and generalizing) existing notions and introducing new, more refi
Externí odkaz:
http://arxiv.org/abs/2306.14978
Autor:
Mokbel, Mohamed, Sakr, Mahmoud, Xiong, Li, Züfle, Andreas, Almeida, Jussara, Anderson, Taylor, Aref, Walid, Andrienko, Gennady, Andrienko, Natalia, Cao, Yang, Chawla, Sanjay, Cheng, Reynold, Chrysanthis, Panos, Fei, Xiqi, Ghinita, Gabriel, Graser, Anita, Gunopulos, Dimitrios, Jensen, Christian, Kim, Joon-Seok, Kim, Kyoung-Sook, Kröger, Peer, Krumm, John, Lauer, Johannes, Magdy, Amr, Nascimento, Mario, Ravada, Siva, Renz, Matthias, Sacharidis, Dimitris, Shahabi, Cyrus, Salim, Flora, Sarwat, Mohamed, Schoemans, Maxime, Speckmann, Bettina, Tanin, Egemen, Teng, Xu, Theodoridis, Yannis, Torp, Kristian, Trajcevski, Goce, van Kreveld, Marc, Wenk, Carola, Werner, Martin, Wong, Raymond, Wu, Song, Xu, Jianqiu, Youssef, Moustafa, Zeinalipour, Demetris, Zhang, Mengxuan, Zimányi, Esteban
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-tracking technologies, mobility data is collected ubiquitously. In recent ye
Externí odkaz:
http://arxiv.org/abs/2307.05717
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 is to discretize the domain into predefined groups, and compare algorit
Externí odkaz:
http://arxiv.org/abs/2302.12333
Autor:
Kanellos, Ilias, Vergoulis, Thanasis, Sacharidis, Dimitris, Dalamagas, Theodore, Vassiliou, Yannis
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 interest. Hence, approaches to effectively identify papers of high impact
Externí odkaz:
http://arxiv.org/abs/2006.00951
Publikováno v:
In Information Systems June 2023 116