61 citations · 200 across the 13 of their papers we have counts for
18 papers
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.…
LSTM Based Sentiment Analysis for Cryptocurrency Prediction
Xin Huang, Wenbin Zhang, Xuejiao Tang +5
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing u…
A Data-driven Human Responsibility Management System
Xuejiao Tang, Jiong Qiu, Ruijun Chen +6
An ideal safe workplace is described as a place where staffs fulfill responsibilities in a well-organized order, potential hazardous events are being monitored in real-time, as wel…
Using Machine Learning to Automate Mammogram Images Analysis
Xuejiao Tang, Liuhua Zhang, Wenbin Zhang +6
Breast cancer is the second leading cause of cancer-related death after lung cancer in women. Early detection of breast cancer in X-ray mammography is believed to have effectively…
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…
FAE: A Fairness-Aware Ensemble Framework
Vasileios Iosifidis, Besnik Fetahu, Eirini Ntoutsi
Automated decision making based on big data and machine learning (ML) algorithms can result in discriminatory decisions against certain protected groups defined upon personal data…
Bias in Data-driven AI Systems -- An Introductory Survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju +20
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and any…
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…
Simple-ML: Towards a Framework for Semantic Data Analytics Workflows
Simon Gottschalk, Nicolas Tempelmeier, Günter Kniesel +3
In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of…
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…
Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives
Pavlos Fafalios, Vasileios Iosifidis, Kostas Stefanidis +1
How did the popularity of the Greek Prime Minister evolve in 2015? How did the predominant sentiment about him vary during that period? Were there any controversial sub-periods? Wh…
TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets
Pavlos Fafalios, Vasileios Iosifidis, Eirini Ntoutsi +1
Publicly available social media archives facilitate research in a variety of fields, such as data science, sociology or the digital humanities, where Twitter has emerged as one of…
Time-Aware and Corpus-Specific Entity Relatedness
Nilamadhaba Mohapatra, Vasileios Iosifidis, Asif Ekbal +2
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for i…