Summary of Fair, Manipulation-robust, and Transparent Sortition, by Carmel Baharav et al.
Fair, Manipulation-Robust, and Transparent Sortition
by Carmel Baharav, Bailey Flanigan
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers investigate sortition algorithms for selecting participants in deliberative processes like Citizens’ Assemblies. These algorithms must balance representation quotas with equal selection chances for volunteers. The authors examine convex equality objectives, including Minimax and Leximin, which minimize or maximize the chance of selection, respectively. However, recent work has highlighted weaknesses in both objectives: Minimax prioritizes fairness but is manipulable, while Leximin focuses on fairness but can be robust to manipulation. The paper aims to identify the most suitable objective for sortition algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sortition is a way to choose people to help make important decisions. It’s like picking a team to represent different groups in society. Researchers are trying to find the best way to do this while making sure everyone has an equal chance of being chosen. They looked at two ways: one that tries to minimize the biggest difference and another that tries to maximize the smallest chance. But these methods have problems – one is good at preventing cheating but unfair, and the other is very fair but can be manipulated. The goal is to find a way that balances fairness and equality. |
