38 citations · 90 across the 19 of their papers we have counts for
21 papers
FairLMs: A Turnkey Library for Fairness in Language Models
Jiale Zhang, Michael Larionov, Zichong Wang +2
Fairness research on language models involves measuring bias, applying mitigation methods, and examining the evidence on which an evaluation rests. Existing tools offer complementa…
Fairness-Aware Graph Representation Learning with Limited Demographic Information
Zichong Wang, Zhipeng Yin, Liping Yang +4
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning meth…
AI Fairness Beyond Complete Demographics: Current Achievements and Future Directions
Zichong Wang, Zhipeng Yin, Roland H. C. Yap +1
Fairness in artificial intelligence (AI) has become a growing concern due to discriminatory outcomes in AI-based decision-making systems. While various methods have been proposed t…
Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications
Shamim Yazdani, Akansha Singh, Nripsuta Saxena +6
In recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been…
Towards Transparent AI: A Survey on Explainable Language Models
Avash Palikhe, Zichong Wang, Zhipeng Yin +4
Language Models (LMs) have significantly advanced natural language processing and enabled remarkable progress across diverse domains, yet their black-box nature raises critical con…
AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models
Zhipeng Yin, Zichong Wang, Avash Palikhe +3
Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models r…
Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification
Jintao Qu, Zichong Wang, Chenhao Wu +1
Neural networks have achieved remarkable success in time series classification, but their reliance on large amounts of labeled data for training limits their applicability in cold-…
Towards Transparent AI: A Survey on Explainable Large Language Models
Avash Palikhe, Zhenyu Yu, Zichong Wang +1
Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decisio…
Datasets for Fairness in Language Models: An In-Depth Survey
Jiale Zhang, Zichong Wang, Avash Palikhe +2
Despite the growing reliance on fairness benchmarks to evaluate language models, the datasets that underpin these benchmarks remain critically underexamined. This survey addresses…
Digital Forensics in the Age of Large Language Models
Zhipeng Yin, Zichong Wang, Weifeng Xu +4
Digital forensics plays a pivotal role in modern investigative processes, utilizing specialized methods to systematically collect, analyze, and interpret digital evidence for judic…
Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL
Zhibo Chu, Zichong Wang, Qitao Qin
Large Language Models (LLMs) exhibit impressive problem-solving skills across many tasks, but they still underperform compared to humans in various downstream applications, such as…
Fairness in Large Language Models in Three Hours
Thang Doan Viet, Zichong Wang, Minh Nhat Nguyen +1
Large Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes agai…
AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias
Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe +5
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmol…
FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications
Zhipeng Yin, Sribala Vidyadhari Chinta, Zichong Wang +2
The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently en…
Fairness Definitions in Language Models Explained
Zhipeng Yin, Zichong Wang, Avash Palikhe +1
Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify soci…
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
Archer Amon, Zhipeng Yin, Zichong Wang +2
Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent con…
Fairness in Large Language Models: A Taxonomic Survey
Zhibo Chu, Zichong Wang, Wenbin Zhang
Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of…
History, Development, and Principles of Large Language Models-An Introductory Survey
Zichong Wang, Zhibo Chu, Thang Viet Doan +3
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.…
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…