Summary of Towards Stable Preferences For Stakeholder-aligned Machine Learning, by Haleema Sheraz et al.
Towards Stable Preferences for Stakeholder-aligned Machine Learning
by Haleema Sheraz, Stefan C. Kremer, Joshua August Skorburg, Graham Taylor, Walter Sinnott-Armstrong, Kyle Boerstler
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 study aims to develop a data-driven approach to kidney allocation that incorporates stakeholder values. It creates machine learning models to predict individual and group-level preferences for kidney allocations using data from the ‘Pairwise Kidney Patient Online Survey’. The models are assessed across three levels: Individual, Group, and Stability. This research aspires to advance ethical dimensions of organ transplantation by promoting transparent and equitable practices while integrating moral values into algorithmic decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a machine learning solution for kidney allocation that considers stakeholder values. It uses data from an online survey to predict individual and group preferences. The researchers create three models: one for individuals, one for groups, and one to evaluate stability over time. By incorporating these values into the process, they hope to make organ transplantation more fair and transparent. |
Keywords
* Artificial intelligence * Machine learning
