论文标题
在非IID图中的公平性数据：文献综述
Fairness Amidst Non-IID Graph Data: A Literature Review
论文作者
论文摘要
理解和解决人工智能（AI）中算法偏见的重要性越来越多，导致对AI公平性的研究激增，这通常假设基础数据是独立的且分布相同的（IID）。但是，实际数据经常存在于捕获单个单元之间连接的非IID图结构中。为了有效地减轻AI系统的偏见，必须弥合用于IID数据的传统公平文献之间的差距，并弥合非IID图数据的流行率。这项调查回顾了非IID图数据中的公平性进步，包括新引入的公平图生成和经常研究的公平图分类。此外，确定了未来研究的可用数据集和评估指标，强调了现有工作的局限性，并提出了有希望的未来方向。
The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data is independent and identically distributed (IID). However, real-world data frequently exists in non-IID graph structures that capture connections among individual units. To effectively mitigate bias in AI systems, it is essential to bridge the gap between traditional fairness literature, designed for IID data, and the prevalence of non-IID graph data. This survey reviews recent advancements in fairness amidst non-IID graph data, including the newly introduced fair graph generation and the commonly studied fair graph classification. In addition, available datasets and evaluation metrics for future research are identified, the limitations of existing work are highlighted, and promising future directions are proposed.
