Summary of Fairness-aware Interpretable Modeling (faim) For Trustworthy Machine Learning in Healthcare, by Mingxuan Liu et al.
Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
by Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Fairness-Aware Interpretable Modeling (FAIM) framework aims to improve model fairness in high-stakes fields like healthcare without compromising performance. FAIM features an interactive interface for identifying “fairer” models and integrating data-driven evidence with clinical expertise to enhance contextualized fairness. The framework was demonstrated on two real-world databases, reducing sex and race biases in predicting hospital admission. The results showed that FAIM models exhibited satisfactory discriminatory performance while significantly mitigating biases as measured by established fairness metrics, outperforming commonly used bias-mitigation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to make machine learning models fairer without losing their good performance. They call it Fairness-Aware Interpretable Modeling (FAIM). It lets experts work together to pick the fairest model from many good ones. The team tested FAIM on two big databases and found that it reduced biases in predicting hospital admission. This is important because AI should be fair and not discriminate against certain groups. |
Keywords
* Artificial intelligence * Machine learning
