4 citations · 4 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
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…