Summary of Minimax Group Fairness in Strategic Classification, by Emily Diana et al.
Minimax Group Fairness in Strategic Classification
by Emily Diana, Saeed Sharifi-Malvajerdi, Ali Vakilian
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores strategic classification, where agents modify their features to receive a favorable outcome from the learner’s classifier. The learner aims to develop a robust classifier that is resistant to these manipulations while achieving desired performance metrics. This study diverges from traditional approaches by considering learning objectives that not only prioritize accuracy but also ensure group fairness guarantees. Specifically, the paper focuses on the minimax group fairness notion, which seeks to minimize the maximum error rate across population groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a world where people try to manipulate their features to get a good grade from an AI classifier, this research helps the AI learn to make fair decisions that don’t favor one group over another. The goal is to create an AI that’s good at both getting things right and being fair. |
Keywords
* Artificial intelligence * Classification
