38 citations · 90 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
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-…
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…
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…
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…
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…