Contemporary AI regulation, including the EU Artificial Intelligence Act and related governance frameworks, increasingly requires institutions to justify the training data used in automated decision-making. Yet existing governance regimes provide limited operational methods for selecting, weighting, and explaining data inputs. We introduce the Smart Data Portfolio (SDP) framework, which treats data categories as productive but risk-bearing assets, formalizing input governance as an information-risk trade-off. Within this framework, we define two portfolio-level quantities, Informational Return and Governance-Adjusted Risk, whose interaction characterizes attainable data mixtures and yields a Governance-Efficient Frontier. Regulators shape this frontier through risk caps, admissible categories, and weight bands that translate fairness, privacy, robustness, and provenance requirements into measurable constraints on data allocation while preserving model flexibility. A sectoral illustration shows how different AI services require distinct portfolios within a common governance structure. The framework provides an input-level explanation layer through which institutions can justify governed data use in large-scale AI deployment.

翻译：当代人工智能监管，包括欧盟《人工智能法案》及相关治理框架，日益要求机构对自动化决策中使用的训练数据进行合理解释。然而，现有治理机制在数据输入的选择、加权和解释方面提供的可操作方法有限。本文提出智能数据组合框架，该框架将数据类别视为具有生产力但承担风险的资产，将输入治理形式化为信息与风险的权衡。在此框架内，我们定义了两个组合层面的量化指标——信息回报与治理调整风险，二者的相互作用刻画了可达的数据混合状态，并产生一条治理有效前沿。监管机构通过风险上限、可接受类别和权重区间来塑造该前沿，这些约束将公平性、隐私性、稳健性和来源可溯性要求转化为数据分配的可度量限制，同时保持模型灵活性。一个行业案例展示了不同AI服务如何在共同治理结构下需要不同的数据组合。该框架提供了一个输入层面的解释层，使机构能够为大规模AI部署中受治理的数据使用提供合理解释。