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Smart Data Portfolios: A Quantitative Framework for Input Governance in AI

A. Talha Yalta, A. Yasemin Yalta

TL;DR

The paper addresses the gap in AI governance for explaining data inputs by treating data categories as regulated assets. It introduces the Smart Data Portfolio (SDP) framework, defining Informational Return and Governance-Adjusted Risk and deriving a Governance-Efficient Frontier to formalize input governance under regulatory caps, admissible categories, and weight bands. The approach provides a model-agnostic, input-level explanation layer and a suite of reporting artifacts (Data Portfolio Statement, Data Portfolio Card, Consumer Portfolio Report) for auditable deployment. A telecom sector illustration demonstrates how heterogeneous data and use cases can be governed within a shared framework, with practical implications for policy integration and scalable governance.

Abstract

Growing concerns about fairness, privacy, robustness, and transparency have made it a central expectation of AI governance that automated decisions be explainable by institutions and intelligible to affected parties. 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 data mixtures and generates 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 telecommunications illustration shows how different AI services require distinct portfolios within a common governance structure. The framework offers a familiar portfolio logic as an input-level explanation layer suited to the large-scale deployment of AI systems.

Smart Data Portfolios: A Quantitative Framework for Input Governance in AI

TL;DR

The paper addresses the gap in AI governance for explaining data inputs by treating data categories as regulated assets. It introduces the Smart Data Portfolio (SDP) framework, defining Informational Return and Governance-Adjusted Risk and deriving a Governance-Efficient Frontier to formalize input governance under regulatory caps, admissible categories, and weight bands. The approach provides a model-agnostic, input-level explanation layer and a suite of reporting artifacts (Data Portfolio Statement, Data Portfolio Card, Consumer Portfolio Report) for auditable deployment. A telecom sector illustration demonstrates how heterogeneous data and use cases can be governed within a shared framework, with practical implications for policy integration and scalable governance.

Abstract

Growing concerns about fairness, privacy, robustness, and transparency have made it a central expectation of AI governance that automated decisions be explainable by institutions and intelligible to affected parties. 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 data mixtures and generates 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 telecommunications illustration shows how different AI services require distinct portfolios within a common governance structure. The framework offers a familiar portfolio logic as an input-level explanation layer suited to the large-scale deployment of AI systems.

Paper Structure

This paper contains 22 sections, 17 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Related Literature
  3. Transparency and explainability
  4. Fairness and bias
  5. Privacy and data protection
  6. Robustness and data quality
  7. Value of information and efficiency
  8. Governance frameworks and emerging principles
  9. Governance frameworks and emerging principles
  10. The Smart Data Portfolio Framework
  11. Methodology
  12. Informational Return
  13. Governance-Adjusted Risk
  14. Governance-Efficient Frontier
  15. Portfolio optimization and regulation
  16. ...and 7 more sections

Figures (1)

  • Figure 1: Illustrative governance-efficient frontier under a policy-defined risk cap. Filled markers denote admissible SDPs. Hollow markers denote feasible but dominated SDPs that lie below the frontier. Points to the right of the Risk Cap are policy-infeasible. The governance-optimal portfolio is the highest-return point on the frontier within the admissible region.