Summary of An Explainablefair Framework For Prediction Of Substance Use Disorder Treatment Completion, by Mary M. Lucas et al.
An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
by Mary M. Lucas, Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang, Jacqueline E. Braughton, Quyen M. Ngo
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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 The proposed study aims to develop a framework that addresses both fairness and explainability in machine learning models used in healthcare. The researchers optimize a model’s performance, then use an in-processing approach to mitigate biases related to race and sex. They also visualize explanations of the changes made to achieve fairness, which can provide insights for clinicians to inform decision-making and resource allocation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study focuses on creating a framework that balances fairness and explainability in healthcare machine learning models. By optimizing model performance and using an in-processing approach to reduce biases, the researchers aim to create more trustworthy models. The resulting models retain high sensitivity while improving fairness and providing explanations for fairness enhancements. |
Keywords
» Artificial intelligence » Machine learning
