71 citations · 131 across the 16 of their papers we have counts for
17 papers
TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
Emmanouil Panagiotou, Benoît Ronval, Arjun Roy +4
Ensuring fairness in machine learning remains a significant challenge, as models often inherit biases from their training data. Generative models have recently emerged as a promisi…
MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs
Swati Swati, Arjun Roy, Emmanouil Panagiotou +1
Fairness-aware classification requires balancing performance and fairness, often intensified by intersectional biases. Conflicting fairness definitions further complicate the task,…
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy +3
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of rea…
Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restoration
Chethan Krishnamurthy Ramanaik, Arjun Roy, Tobias Callies +1
Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-condit…
Achieving Socio-Economic Parity through the Lens of EU AI Act
Arjun Roy, Stavroula Rizou, Symeon Papadopoulos +1
Unfair treatment and discrimination are critical ethical concerns in AI systems, particularly as their adoption expands across diverse domains. Addressing these challenges, the rec…
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
Emmanouil Panagiotou, Arjun Roy, Eirini Ntoutsi
Due to their data-driven nature, Machine Learning (ML) models are susceptible to bias inherited from data, especially in classification problems where class and group imbalances ar…
Adversarial Robustness of VAEs across Intersectional Subgroups
Chethan Krishnamurthy Ramanaik, Arjun Roy, Eirini Ntoutsi
Despite advancements in Autoencoders (AEs) for tasks like dimensionality reduction, representation learning and data generation, they remain vulnerable to adversarial attacks. Vari…
Exploring Fusion Techniques in Multimodal AI-Based Recruitment: Insights from FairCVdb
Swati Swati, Arjun Roy, Eirini Ntoutsi
Despite the large body of work on fairness-aware learning for individual modalities like tabular data, images, and text, less work has been done on multimodal data, which fuses var…
FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning
Arjun Roy, Christos Koutlis, Symeon Papadopoulos +1
The generalisation capacity of Multi-Task Learning (MTL) suffers when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients. This is…
Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview
Arjun Roy, Jan Horstmann, Eirini Ntoutsi
AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The do…
Learning to Teach Fairness-aware Deep Multi-task Learning
Arjun Roy, Eirini Ntoutsi
Fairness-aware learning mainly focuses on single task learning (STL). The fairness implications of multi-task learning (MTL) have only recently been considered and a seminal approa…
Parity-based Cumulative Fairness-aware Boosting
Vasileios Iosifidis, Arjun Roy, Eirini Ntoutsi
Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race. One reason for this behavior is the encoded societal biases in the train…
A survey on datasets for fairness-aware machine learning
Tai Le Quy, Arjun Roy, Vasileios Iosifidis +2
As decision-making increasingly relies on Machine Learning (ML) and (big) data, the issue of fairness in data-driven Artificial Intelligence (AI) systems is receiving increasing at…
Fair-Capacitated Clustering
Tai Le Quy, Arjun Roy, Gunnar Friege +1
Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a f…
Multi-fairness under class-imbalance
Arjun Roy, Vasileios Iosifidis, Eirini Ntoutsi
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced.…
Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
Arjun Roy, Pavlos Fafalios, Asif Ekbal +2
Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. I…
A Deep Ensemble Framework for Fake News Detection and Classification
Arjun Roy, Kingshuk Basak, Asif Ekbal +1
Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. The rate of such…