40 citations · 111 across the 17 of their papers we have counts for
22 papers
The Internet of Responsibilities-Connecting Human Responsibilities using Big Data and Blockchain
Xuejiao Tang, Jiong Qiu, Wenbin Zhang +6
Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a nov…
Preventing Discriminatory Decision-making in Evolving Data Streams
Zichong Wang, Nripsuta Saxena, Tongjia Yu +8
Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making syst…
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
Zichong Wang, Yang Zhou, David Lo +1
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fai…
Individual Fairness under Uncertainty
Wenbin Zhang, Zichong Wang, Juyong Kim +4
Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML. As ML technologies expand their application domains, includi…
Fair Decision-making Under Uncertainty
Wenbin Zhang, Jeremy C. Weiss
There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surpr…
Attention Mechanism based Cognition-level Scene Understanding
Xuejiao Tang, Wenbin Zhang
Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real wo…
Longitudinal Fairness with Censorship
Wenbin Zhang, Jeremy C. Weiss
Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic.…
Fairness Amidst Non-IID Graph Data: A Literature Review
Wenbin Zhang, Shuigeng Zhou, Toby Walsh +1
The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the…
Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
Thomas Guyet, Wenbin Zhang, Albert Bifet
The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Curre…
A Generic Knowledge Based Medical Diagnosis Expert System
Xin Huang, Xuejiao Tang, Wenbin Zhang +6
In this paper, we design and implement a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms. In this system, some important aspects like k…
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…
FARF: A Fair and Adaptive Random Forests Classifier
Wenbin Zhang, Albert Bifet, Xiangliang Zhang +2
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning…
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…
Interpretable Visual Understanding with Cognitive Attention Network
Xuejiao Tang, Wenbin Zhang, Yi Yu +4
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-l…
Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
Xuejiao Tang, Xin Huang, Wenbin Zhang +4
Visual Commonsense Reasoning (VCR) predicts an answer with corresponding rationale, given a question-image input. VCR is a recently introduced visual scene understanding task with…
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…
Online and Customizable Fairness-aware Learning
Wenbin Zhang
While artificial intelligence (AI)-based decision-making systems are increasingly popular, significant concerns on the potential discrimination during the AI decision-making proces…
Disentangled Dynamic Graph Deep Generation
Wenbin Zhang, Liming Zhang, Dieter Pfoser +1
Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of prot…
FAHT: An Adaptive Fairness-aware Decision Tree Classifier
Wenbin Zhang, Eirini Ntoutsi
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms…
A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
Wenbin Zhang, Jianwu Wang, Daeho Jin +2
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space…