Guardrails for Human-AI Ecologies: Norm-Based Coordination and Design for Predictability
研究如何通过社会规范为人机生态设立护栏，整合预测处理与社会规范理论，提出设计原则帮助管理者和开发者编码规范、监控结果并适时干预，确保人机协调的可预测性与安全性。
Human-AI ecologies involve human and AI-based agents that coordinate their interactions in part by following social norms. Social norms, therefore, are important for establishing the guardrails that ensure desirable interactions in a way that is consistent with essential values, such as human safety. Managing human-AI ecologies requires specifying norms to enable coordination in known situations but also allowing for the emergence of norms to enable coordination in unspecified, unstructured situations. We integrate predictive processing theory and social norm theory to explain how existing norms are enacted and reinforced based on agents’ predictive models and how new norms emerge as agents update their predictive models in response to prediction errors in uncertain coordination scenarios. Rooted in this perspective, we develop a design theory that emphasizes design for predictability and propose a set of design principles for managers and developers to encode norms to evolve in human-AI ecologies, monitor outcomes, and intervene when necessary.