Summary of Distributionally Robust Survival Analysis: a Novel Fairness Loss Without Demographics, by Shu Hu et al.
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
by Shu Hu, George H. Chen
First submitted to arxiv on: 18 Nov 2022
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The proposed approach for training survival analysis models minimizes a worst-case error across all subpopulations that are large enough, without using sensitive demographic information in the training loss function. This method is compared to various baselines, including those that directly use sensitive demographic information, and demonstrates improved performance on fairness metrics while maintaining prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way of training survival analysis models that doesn’t take into account sensitive information. The goal is to minimize mistakes in subgroups that are big enough. Surprisingly, this method often performs better than others when it comes to fairness and accuracy. |
Keywords
* Artificial intelligence * Loss function
