Summary of Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups, by Mohsen Dehghankar et al.
Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups
by Mohsen Dehghankar, Abolfazl Asudeh
First submitted to arxiv on: 7 Nov 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 addresses the issue of unidentified minority groups in machine learning datasets. In the wild, data often lacks critical information about groupings, making it challenging for responsible data scientists to identify and consider underrepresented minority groups. The absence of this information can lead to biased models that may not perform well for these groups. The authors propose a solution to this dilemma by developing methods that can infer missing grouping information from existing datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about solving a problem in machine learning where we don’t have enough information about certain groups of people. This makes it hard for us to make sure our models are fair and work well for everyone. The authors want to find a way to figure out which groups are missing, even if we can’t see the group labels. |
Keywords
* Artificial intelligence * Machine learning
