Summary of A Unified Post-processing Framework For Group Fairness in Classification, by Ruicheng Xian et al.
A Unified Post-Processing Framework for Group Fairness in Classification
by Ruicheng Xian, Han Zhao
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed “LinearPost” algorithm is a post-processing method for achieving group fairness in multiclass classification problems, encompassing statistical parity, equal opportunity, and equalized odds under a unified framework. It linearly transforms the predictions of an unfair base predictor based on a weighted combination of predicted group memberships, guaranteeing fairness as long as the group membership predictor is multicalibrated. The algorithm’s parameters can be efficiently computed by solving an empirical linear program. Compared to existing algorithms, “LinearPost” demonstrates advantages in high-fairness regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make sure that a computer system treats different groups fairly is presented. This method, called “LinearPost”, works on top of an initial prediction made by another algorithm and adjusts it to meet certain fairness standards. The goal is to ensure that the system’s decisions are fair for everyone, regardless of their group membership. This approach has been tested and shown to perform well in situations where high levels of fairness are required. |
Keywords
» Artificial intelligence » Classification
