Fairness Amidst Non-IID Graph Data: A Literature Review
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866916582083526656 |
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| author | Zhang, Wenbin Zhou, Shuigeng Walsh, Toby Weiss, Jeremy C. |
| author_facet | Zhang, Wenbin Zhou, Shuigeng Walsh, Toby Weiss, Jeremy C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_07170 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Fairness Amidst Non-IID Graph Data: A Literature Review Zhang, Wenbin Zhou, Shuigeng Walsh, Toby Weiss, Jeremy C. Machine Learning Artificial Intelligence 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. |
| title | Fairness Amidst Non-IID Graph Data: A Literature Review |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2202.07170 |