Summary of Investigating Gender Fairness in Machine Learning-driven Personalized Care For Chronic Pain, by Pratik Gajane and Sean Newman and Mykola Pechenizkiy and John D. Piette
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain
by Pratik Gajane, Sean Newman, Mykola Pechenizkiy, John D. Piette
First submitted to arxiv on: 29 Feb 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 This paper explores the use of reinforcement learning (RL) in personalizing pain management interventions for individuals with chronic pain. The authors aim to address concerns about RL exacerbating disparities based on patient characteristics like race or gender. Specifically, they investigate gender fairness in personalized pain care recommendations using a real-world application of RL. They find that included features can impact gender fairness and propose an RL solution, NestedRecommendation, that adapts to optimize for utility and fairness while accelerating feature selection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses reinforcement learning (RL) to help people with chronic pain get better treatment. It wants to make sure that the treatment is fair for everyone, no matter what they look like or where they come from. The authors tested different things they use to help decide what treatment to give and found that some of these things can affect how fair it is. They also created a new way to do RL that makes sure the treatment is good and fair, and that gets better over time. |
Keywords
* Artificial intelligence * Feature selection * Reinforcement learning
