Smart Data Portfolios: A Governance Framework for AI Training Data

TL;DR

The paper introduces the Smart Data Portfolio framework, a novel approach to AI training data governance that balances information value and risk, enabling transparent and regulated data management for AI models.

Contribution

It formalizes data governance as a portfolio optimization problem, integrating fairness, privacy, and robustness constraints into a measurable, flexible framework for AI training data management.

Findings

Defines Informational Return and Governance-Adjusted Risk metrics

Introduces a Governance-Efficient Frontier for data portfolios

Demonstrates sectoral application of the framework

Abstract

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.