Summary of Monotone Individual Fairness, by Yahav Bechavod
Monotone Individual Fairness
by Yahav Bechavod
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 revisits online learning with individual fairness, aiming to maximize predictive accuracy while ensuring similar individuals are treated similarly. Building upon existing frameworks, this study introduces monotone aggregation functions for auditing schemes that aggregate feedback from multiple auditors. The authors prove a characterization of these auditing schemes and present oracle-efficient algorithms achieving upper bounds for regret and number of fairness violations in both full and partial information settings. Their algorithms improve upon the best known bounds and offer significant computational efficiency improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that similar people are treated fairly online. Right now, some algorithms can be unfair even if they’re very good at predicting what will happen. The authors of this study want to fix this problem by creating new ways for these algorithms to learn and improve while staying fair. They introduce new methods that work better than the old ones and use less computer power. This is important because it helps ensure that people are treated equally online. |
Keywords
* Artificial intelligence * Online learning
