Summary of Does Data-efficient Generalization Exacerbate Bias in Foundation Models?, by Dilermando Queiroz et al.
Does Data-Efficient Generalization Exacerbate Bias in Foundation Models?
by Dilermando Queiroz, Anderson Carlos, Maíra Fatoretto, Luis Filipe Nakayama, André Anjos, Lilian Berton
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study investigates the fairness implications of using foundation models for medical diagnoses in Brazil, particularly when pre-trained on large amounts of unlabeled data containing sensitive attributes. The researchers examine whether this approach can reduce the gap between maximum and minimum AUC evaluations across gender and age groups, as well as its performance in a data-efficient generalization setting. They find that while the Foundation Model (RetFound) has the potential to improve fairness, it may actually increase bias when deployed with limited data. This study highlights the importance of considering fairness issues when applying foundation models in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models have become popular for medical diagnoses due to their ability to learn from large amounts of unlabeled data. Researchers studied how these models work when applied to a specific dataset for diagnosing eye problems in Brazil. They found that while the model can improve fairness, it might also introduce bias when used with limited data. This means we need to be careful when using these models to make sure they don’t unfairly favor certain groups. |
Keywords
» Artificial intelligence » Auc » Generalization
