Summary of Dancing in the Shadows: Harnessing Ambiguity For Fairer Classifiers, by Ainhize Barrainkua et al.
Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
by Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto
First submitted to arxiv on: 27 Jun 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 A novel approach is proposed to enhance algorithmic fairness in scenarios where sensitive information is only partially known. By leveraging instances with uncertain identity regarding the sensitive attribute, a conventional machine learning classifier can be trained to produce enhanced fairness in its predictions. The results show promising potential for prioritizing ambiguity as a means to improve fairness guarantees in real-world classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make machine learning models fairer when we don’t know all the sensitive information. They train a normal machine learning model using instances where the sensitive attribute is unclear, and this helps make the model more fair. This could be important for real-life applications where we can’t always get all the information. |
Keywords
* Artificial intelligence * Classification * Machine learning
