Summary of Mitigating Matching Biases Through Score Calibration, by Mohammad Hossein Moslemi et al.
Mitigating Matching Biases Through Score Calibration
by Mohammad Hossein Moslemi, Mostafa Milani
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Databases (cs.DB)
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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 Record matching in domains like healthcare, finance, and e-commerce is crucial for data integration. Traditional models prioritize accuracy but neglect fairness issues, leading to demographic disparities in model performance. This paper focuses on cumulative bias across all thresholds, addressing limitations of existing threshold-specific metrics. A novel post-processing calibration method using optimal transport theory and Wasserstein barycenters balances matching scores across demographic groups, treating any matching model as a black box. The approach is applicable to various models without access to training data. Experimental results demonstrate the effectiveness in reducing demographic parity difference. To address EOD and EO differences, the paper introduces a conditional calibration method, achieving fairness across benchmarks and state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to match different records from different databases that are actually talking about the same thing. This is called record matching, and it’s important in fields like healthcare and finance. Right now, many models focus on being accurate but don’t consider fairness. This means some groups might get more wrong answers than others. The authors of this paper want to fix this by creating a new way to make sure the records match fairly across all groups. They tested their method with different models and found that it works well. |
