Responsible AI

DESCRIPTION

Machine Learning methods rely heavily on supervision, i.e. the provision of “labels” or “annotations” in the training data. And, while they are impressive in identifying patterns that are useful for making predictions, they are not as successful in identifying causal relationships among the relevant variables so as to be able to make good counterfactual predictions. While causal inference is a widely studied field, there remain important challenges in bringing existing techniques to bear in the high-dimensional, non-parametric, and non-asymptotic sample settings of relevance to modern learning applications. We develop these foundations using a variety of approaches from high-dimensional Statistics, Econometrics and Machine Learning. We also use our techniques to improve model performance under distribution shift, decrease the level of supervision that is needed to train models, obtain models that generalize better to unseen and multi-modal data, and decrease the bias baked into trained models and the unfairness caused by their deployment.

The issue of algorithmic bias and fairness appears also in different contexts. We consider fairness for graphs. Graphs are a ubiquitous data model for entities, their relations, dependencies, and interactions. There are also multiple real-world graphs, ranging from social, communication and transportation networks to biological networks and the brain. We study the representation bias in graph data, as a result of data collection, and its effect on graph algorithms. We also consider different processes that happen on graphs, such as temporal evolution, ranking processes, information diffusion and opinion formation, and study their fairness. Finally, we explore explanations for bias of graph algorithms, where the goal is to explain the bias towards groups rather than individuals.

RESEARCHERS

antonios-anastasopoulos
Antonios Anastasopoulos
GEORGE MASON UNIVERSITY
archontis-giannakidis
Archontis Giannakidis
Nottingham Trent University
aris-pagourtzis
Aris Pagourtzis
National Technical University of Athens
dimitrios-kanoulas
Dimitrios Kanoulas
University College London
evaggelia-pitoura
Evaggelia Pitoura
University of Ioannina
grigorios-tsoumakas
Grigorios Tsoumakas
Aristotle University of Thessaloniki
kostas-athanasakis
Kostas Athanasakis
University of West Attica
nikos-vasilakis
Nikos Vasilakis
Brown University
panagiotis-tsaparas
Panagiotis Tsaparas
University of Ioannina
sotirios-tsaftaris
Sotirios Tsaftaris
THE UNIVERSITY OF EDINBURGH
stefanos-nikolaidis
Stefanos Nikolaidis
University of Southern California
stella-markantonatou
Stella Markantonatou
Research Center Athena
vasilis-gkatzelis
Vasilis Gkatzelis
Drexel University
 
 

The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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