Summary of Demographic Parity in Regression and Classification Within the Unawareness Framework, by Vincent Divol (ensae Paris) et al.
Demographic parity in regression and classification within the unawareness framework
by Vincent Divol, Solenne Gaucher
First submitted to arxiv on: 4 Sep 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 This paper delves into the theoretical underpinnings of fair regression within the unawareness framework, where demographic parity is ensured. The authors investigate the optimal fair regression function when minimizing quadratic loss, revealing that it corresponds to the solution of a barycenter problem with optimal transport costs. Additionally, they explore connections between cost-sensitive classification and regression, demonstrating that nested decision sets establish an equivalence between both tasks. Under this assumption, optimal classifiers can be derived by thresholding optimal fair regression functions, while optimal regression is characterized by families of cost-sensitive classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure a machine learning model treats everyone fairly. It’s like playing a game where you want to make sure all players have an equal chance of winning. The researchers looked at how to find the best way to make a model fair when it’s trying to predict something. They found that the best way is by solving a special kind of math problem. They also showed that two other important problems in machine learning are actually connected, and that makes things easier to understand. Overall, this research helps us create more fair and equal machine learning models. |
Keywords
» Artificial intelligence » Classification » Machine learning » Regression
