Summary of Debias-clr: a Contrastive Learning Based Debiasing Method For Algorithmic Fairness in Healthcare Applications, by Ankita Agarwal et al.
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications
by Ankita Agarwal, Tanvi Banerjee, William Romine, Mia Cajita
First submitted to arxiv on: 15 Nov 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 The proposed implicit in-processing debiasing method, called Debias-CLR, aims to mitigate demographic biases within machine learning models trained on clinical notes. By using deep learning contrastive learning frameworks for gender and ethnicity, the model learns to reduce Single-Category Word Embedding Association Test (SC-WEAT) effect size scores. The authors train two separate frameworks, one for gender and another for ethnicity, using feature embeddings from Clinical BERT and LSTM autoencoders. They demonstrate that Debias-CLR can reduce demographic biases without compromising accuracy on downstream tasks like predicting length of stay. This approach has the potential to mitigate health disparities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence models used in healthcare can be biased against certain groups of people, leading to unequal treatment. To fix this, researchers developed a new way to make these models fairer. They created a special method called Debias-CLR that helps remove biases based on gender and ethnicity. The team tested their approach using data from heart failure patients and found it worked well. This means the model can predict things like how long someone will stay in the hospital without being unfair to certain groups. |
Keywords
* Artificial intelligence * Bert * Deep learning * Embedding * Lstm * Machine learning
