61 citations · 200 across the 13 of their papers we have counts for
9 papers · 1 filter
Discrimination and Class Imbalance Aware Online Naive Bayes
Maryam Badar, Marco Fisichella, Vasileios Iosifidis +1
Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to repla…
AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification
Vasileios Iosifidis, Symeon Papadopoulos, Bodo Rosenhahn +1
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority clas…
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…
Online Fairness-Aware Learning with Imbalanced Data Streams
Vasileios Iosifidis, Wenbin Zhang, Eirini Ntoutsi
Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job application…
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.…
FairNN- Conjoint Learning of Fair Representations for Fair Decisions
Tongxin Hu, Vasileios Iosifidis, Wentong Liao +4
In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objectiv…
AdaFair: Cumulative Fairness Adaptive Boosting
Vasileios Iosifidis, Eirini Ntoutsi
The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential…
Fairness-enhancing interventions in stream classification
Vasileios Iosifidis, Thi Ngoc Han Tran, Eirini Ntoutsi
The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of…