集体智能的（不）可能性
(Im)possibility of Collective Intelligence
AI总结：
该研究将学习算法刻画为假设空间上的选择对应关系，通过公理分析得出异构环境中唯一理性学习算法是单环境ERM的结论，揭示了实现跨环境集体智能面临的根本权衡与固有困难。
摘要：
现代AI应用涉及在异构且可能规模庞大的环境中训练和部署机器学习模型。不断涌现的数据多样性不仅为推进AI系统带来了新的可能，也因隐私、安全和公平性等紧迫问题，限制了环境间可共享的信息范围。本研究将学习算法全新刻画为假设空间上的选择对应关系，在此基础上提出了一组直观合理的公理作为最低要求，而在该要求下，异构环境中唯一的理性学习算法是仅从单一环境单边学习、不进行跨环境信息共享的经验风险最小化（ERM）。我们的（不）可能性结果凸显了所有算法为实现集体智能（CI，即跨异构环境学习的能力）都将面临的根本权衡。归根结底，异构环境中的集体学习本质上极具难度，因为在分布外泛化、联邦/协同学习、算法公平性和多模态学习等机器学习关键领域，跨环境对模型预测性能进行有意义的比较可能并不可行。
英文摘要：
Modern applications of AI involve training and deploying machine learning models across heterogeneous and potentially massive environments. Emerging diversity of data not only brings about new possibilities to advance AI systems, but also restricts the extent to which information can be shared across environments due to pressing concerns such as privacy, security, and equity. Based on a novel characterization of learning algorithms as choice correspondences on a hypothesis space, this work provides a minimum requirement in terms of intuitive and reasonable axioms under which the only rational learning algorithm in heterogeneous environments is an empirical risk minimization (ERM) that unilaterally learns from a single environment without information sharing across environments. Our (im)possibility result underscores the fundamental trade-off that any algorithms will face in order to achieve Collective Intelligence (CI), i.e., the ability to learn across heterogeneous environments. Ultimately, collective learning in heterogeneous environments are inherently hard because, in critical areas of machine learning such as out-of-distribution generalization, federated/collaborative learning, algorithmic fairness, and multi-modal learning, it can be infeasible to make meaningful comparisons of model predictive performance across environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
