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INSTITUTE: CISPA - Helmholtz Center for Information Security which is within the Helmholtz Association

ABSTRACT: Democratization of AI involves training and deploying machine learning models across heterogeneous and potentially massive environments. While a diversity of data can bring about new possibilities to advance AI systems, it simultaneously restricts the extent to which information can be shared across environments due to pressing concerns such as privacy, security, and equity. Inspired by the social choice theory, I will first present a choice-theoretic perspective of machine learning as a tool to analyze learning algorithms. To understand the fundamental limits, I will then provide a minimum requirement in terms of intuitive and reasonable axioms under which an empirical risk minimization (ERM) that learns from a single environment is the only rational learning algorithm in heterogeneous environments. This impossibility result implies that Collective Intelligence (CI), the ability of algorithms to successfully learn across heterogeneous environments, cannot be achieved without sacrificing at least one of these essential properties. Lastly, I will discuss the implications of this result in critical areas of machine learning such as out-of-distribution generalization, federated learning, algorithmic fairness, and multi-modal learning.