Combating misinformation in the age of llms: Opportunities and challenges

C Chen, K Shu - AI Magazine, 2024‏ - Wiley Online Library
Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Recommender systems in the era of large language models (llms)

Z Zhao, W Fan, J Li, Y Liu, X Mei, Y Wang… - arxiv preprint arxiv …, 2023‏ - arxiv.org
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …

A survey on rag meeting llms: Towards retrieval-augmented large language models

W Fan, Y Ding, L Ning, S Wang, H Li, D Yin… - Proceedings of the 30th …, 2024‏ - dl.acm.org
As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

Membership inference attacks on machine learning: A survey

H Hu, Z Salcic, L Sun, G Dobbie, PS Yu… - ACM Computing Surveys …, 2022‏ - dl.acm.org
Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Fairness in machine learning: A survey

S Caton, C Haas - ACM Computing Surveys, 2024‏ - dl.acm.org
When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability

E Dai, T Zhao, H Zhu, J Xu, Z Guo, H Liu, J Tang… - Machine Intelligence …, 2024‏ - Springer
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Trustworthy AI: From principles to practices

B Li, P Qi, B Liu, S Di, J Liu, J Pei, J Yi… - ACM Computing Surveys, 2023‏ - dl.acm.org
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

A survey on trustworthy edge intelligence: From security and reliability to transparency and sustainability

X Wang, B Wang, Y Wu, Z Ning… - … Surveys & Tutorials, 2024‏ - ieeexplore.ieee.org
Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push
the capabilities of AI to the network edge for real-time, efficient and secure intelligent …

Trustworthy graph neural networks: Aspects, methods and trends

H Zhang, B Wu, X Yuan, S Pan, H Tong… - arxiv preprint arxiv …, 2022‏ - arxiv.org
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

A survey on trustworthy recommender systems

Y Ge, S Liu, Z Fu, J Tan, Z Li, S Xu, Y Li, Y **an… - ACM Transactions on …, 2024‏ - dl.acm.org
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …