Summary of Regression Under Demographic Parity Constraints Via Unlabeled Post-processing, by Evgenii Chzhen (lmo et al.
Regression under demographic parity constraints via unlabeled post-processing
by Evgenii Chzhen, Mohamed Hebiri, Gayane Taturyan
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 paper proposes a novel post-processing algorithm to ensure demographic parity in regression tasks without access to sensitive attributes during inference. The method, which involves discretization and stochastic minimization of a smooth convex function, generates predictions that meet the demographic parity constraint. Unlike prior methods, this approach is fully theory-driven and relies on more advanced techniques than standard stochastic gradient descent. The algorithm is suitable for online post-processing and multi-class classification tasks only involving unlabeled data for the post-processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps fix a problem where regression predictions are not fair to different groups of people without knowing sensitive information. They came up with an algorithm that makes sure predictions meet fairness criteria, even when you don’t have this sensitive information. The method uses some fancy math and techniques to make it work. It’s useful for situations where you need to make predictions online or for multiple classes, but only know the answers are correct. |
Keywords
» Artificial intelligence » Classification » Inference » Regression » Stochastic gradient descent
