Summary of Fairsample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently, by Zicun Cong et al.
FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
by Zicun Cong, Shi Baoxu, Shan Li, Jaewon Yang, Qi He, Jian Pei
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a framework called FairSample that tackles the challenge of training fair and accurate Graph Convolutional Neural Networks (GCNs) efficiently. The authors highlight the importance of fairness in GCNs as they are adopted in many crucial applications, where societal biases against sensitive groups may exist. They adopt the well-known fairness notion of demographic parity and analyze how graph structure bias, node attribute bias, and model parameters affect the demographic parity of GCNs. To mitigate these biases, FairSample employs two intuitive strategies: injecting edges across nodes that are in different sensitive groups but similar in node features, and developing a learnable neighbor sampling policy using reinforcement learning. Additionally, the framework is complemented by a regularization objective to optimize fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to make sure that Graph Convolutional Neural Networks (GCNs) don’t have biases towards certain groups of people or things. This is important because GCNs are used in many areas where it’s crucial to be fair. The team looks at how different kinds of biases can affect the fairness of GCNs and develops a new way to make them more fair, called FairSample. They do this by adding connections between nodes that are similar but belong to different groups, and by using machine learning to decide which neighbors to use. This helps make sure that the GCNs aren’t biased. |
Keywords
* Artificial intelligence * Machine learning * Regularization * Reinforcement learning
