Summary of Representative Social Choice: From Learning Theory to Ai Alignment, by Tianyi Qiu
Representative Social Choice: From Learning Theory to AI Alignment
by Tianyi Qiu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
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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 representative social choice framework is a novel approach for modeling democratic representation in collective decisions where there are too many issues and individuals to consider all preferences directly. This framework can be applied in various real-world decision-making processes such as jury trials, indirect elections, legislation processes, corporate governance, and language model alignment. By treating the population as a finite sample of individual-issue pairs, social choice decisions are made based on these pairs. The study shows that many questions in representative social choice can be formulated as statistical learning problems and proves the generalization properties of social choice mechanisms using machine learning theory. Additionally, axioms for representative social choice are formulated, and Arrow-like impossibility theorems are proved with new combinatorial tools of analysis. This framework opens up research directions at the intersection of social choice, learning theory, and AI alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make fair decisions when many people have different opinions. Imagine you’re part of a group trying to decide what to do, but there are too many options and too many people to consider all their thoughts. That’s where the representative social choice framework comes in. It helps us make decisions by taking a smaller group of people and issues and using that to make choices for everyone else. This is important because it can be used in many real-life situations, like voting or making laws. The study shows how we can use machine learning to understand what makes this framework work well and what are the limitations. |
Keywords
» Artificial intelligence » Alignment » Generalization » Language model » Machine learning
