Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents er-ror detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea.
What's Pulling the Strings? Evaluating Integrity and Attribution in AI Training and Inference through Concept Shift
University of New South Wales \& CSIRO's Data61 Sydney Australia；Macquarie University Sydney Australia；University of New South Wales Sydney Australia；University of Illinois at Urbana–Champaign Champaign；University of New South Wales \& CSIRO's Data61；Macquarie University；University of New South Wales；University of Illinois at Urbana–Champaign
Invisible Manipulation Channels in AI-Assisted Financial Advisory: Implications for Market Integrity and Regulatory Design
College of Economics and Management, Northwest A&F University（西北农林科技大学经济管理学院）；School of Agricultural Economics and Rural Development, Renmin University of China（中国人民大学农业经济与农村发展学院）；Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive, School of Electronic, Electrical and Physics, Fujian University of Technology（福建理工大学电子电气与物理学院，福建省汽车电子与电驱动重点实验室）
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity
University of Granada（格拉纳达大学）；ADIA Lab（ADIA实验室）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所（DaSCI））
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
University of Washington（华盛顿大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）
