9 papers
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…
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…
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…
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…
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…