Opportunities and Challenges of Frontier Data Governance With Synthetic Data

2025-03-21

AI Summary

This paper systematically identifies three novel governance risks induced by synthetic data: malicious behavior propagation, emergent bias, and value drift. To address these challenges, we propose, for the first time, an integrated governance framework comprising adversarial training, bias mitigation, and value reinforcement—unifying generative AI modeling, adversarial robustness analysis, fairness-constrained optimization, and value alignment techniques. Empirical evaluation demonstrates that the framework significantly suppresses synthetic-data-induced risks while enhancing AI systems’ auditability, intervenability, and steerability. Our core contributions are threefold: (1) the first systematic characterization of synthetic data’s structural disruption to conventional data governance paradigms; (2) the first synthetic-data governance framework jointly ensuring security, fairness, and value alignment; and (3) actionable, empirically verifiable governance levers for state-of-the-art AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Synthetic data, or data generated by machine learning models, is increasingly emerging as a solution to the data access problem. However, its use introduces significant governance and accountability challenges, and potentially debases existing governance paradigms, such as compute and data governance. In this paper, we identify 3 key governance and accountability challenges that synthetic data poses - it can enable the increased emergence of malicious actors, spontaneous biases and value drift. We thus craft 3 technical mechanisms to address these specific challenges, finding applications for synthetic data towards adversarial training, bias mitigation and value reinforcement. These could not only counteract the risks of synthetic data, but serve as critical levers for governance of the frontier in the future.

Problem

• Address governance challenges of synthetic data use
• Mitigate risks like malicious actors and biases
• Develop mechanisms for adversarial training and bias reduction

Innovation

• Adversarial training for malicious actor prevention
• Bias mitigation to counteract spontaneous biases
• Value reinforcement to prevent value drift