Summary of Fairm: Learning Invariant Representations For Algorithmic Fairness and Domain Generalization with Minimax Optimality, by Sai Li and Linjun Zhang
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
by Sai Li, Linjun Zhang
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 The proposed FAIRM framework aims to address issues of algorithmic fairness and domain generalization by utilizing invariant principles. The authors introduce a training environment-based oracle, FAIRM, which has desirable properties for fairness and domain generalization under certain conditions. A finite-sample theoretical guarantee is provided, along with efficient algorithms for linear models, ensuring minimax optimality. Experimental results on synthetic data and MNIST demonstrate the superior performance of FAIRM compared to its counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach to machine learning that makes sure it’s fair and works well across different situations. The authors create a special tool called FAIRM that helps with this problem. They show that their method is better than others in certain situations, which could be important for things like image recognition or decision-making. |
Keywords
* Artificial intelligence * Domain generalization * Machine learning * Synthetic data
