AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases, opacity, and other adverse effects. This paper discusses how key requirements for trustworthy AI can be translated into design choices for the components of ERM. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.

翻译：人工智能系统日益影响着个人和社会领域的关键决策。虽然经验风险最小化（ERM）推动了人工智能的诸多成功，但其通常将准确性置于可信度之上，往往导致偏见、不透明性及其他负面影响。本文探讨如何将可信人工智能的关键需求转化为ERM各组成部分的设计选择。我们期望为构建符合新兴人工智能可信标准的系统提供可操作的指导。