Methods and Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment
#arXiv #differentiable social choice #machine learning #AI alignment #federated learning #preference aggregation #resource allocation
📌 Key Takeaways
- Social choice theory is transitioning from a theoretical political framework to a core component of machine learning architecture.
- The integration of preference aggregation is essential for the alignment of Large Language Models (LLMs) and participatory governance.
- Differentiable social choice enables systems like federated learning and auctions to better handle heterogeneous human incentives.
- The paper identifies significant open problems regarding how AI models can ethically and efficiently translate individual judgments into collective outcomes.
📖 Full Retelling
Researchers specializing in artificial intelligence and economic theory published a comprehensive survey on the arXiv preprint server in February 2024 to address the critical integration of differentiable social choice into modern machine learning systems. This technical overview, titled "Methods and Open Problems in Differentiable Social Choice," explores how classical social choice theory—historically a niche area of political science and economics—has evolved into a foundational pillar for contemporary AI. The authors argue that this shift is necessary because modern software pipelines must now aggregate diverse human preferences and incentives to make collective, automated decisions in increasingly complex digital environments.
🏷️ Themes
Artificial Intelligence, Social Choice Theory, Machine Learning
📚 Related People & Topics
AI alignment
Conformance of AI to intended objectives
In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.
🔗 Entity Intersection Graph
Connections for AI alignment:
- 🌐 Large language model (2 shared articles)
- 🌐 Reinforcement learning (1 shared articles)
- 🌐 PPO (1 shared articles)
- 🌐 Sleeper agent (1 shared articles)
- 🌐 Situation awareness (1 shared articles)
- 🌐 Reinforcement learning from human feedback (1 shared articles)
- 🌐 Government of France (1 shared articles)
📄 Original Source Content
arXiv:2602.03003v2 Announce Type: replace Abstract: Social choice is no longer a peripheral concern of political theory or economics-it has become a foundational component of modern machine learning systems. From auctions and resource allocation to federated learning, participatory governance, and the alignment of large language models, machine learning pipelines increasingly aggregate heterogeneous preferences, incentives, and judgments into collective decisions. In effect, many contemporary m