Neither Consent nor Property: A Policy Lab for Data Law

2025-10-30

AI Summary

This paper addresses three structural challenges in AI-driven data markets: systemic opacity, price discovery failure, and fragile technical safeguards. To tackle them, we develop a high-fidelity computational experimental platform integrating ethnographic fieldwork, large language model–driven discrete choice experiments, and multi-agent simulation. Our key theoretical contribution is the “minimum-cost risk-avoider” mechanism, which demonstrates that internalizing data-related risks by downstream buyers—rather than relying on ex ante property rights—maximizes social welfare, thereby challenging conventional产权-based regulatory paradigms. Empirically, the platform successfully replicates real-world trading patterns. It reveals that while anonymization exemptions expand transaction volume, they precipitate social welfare collapse; in contrast, risk internalization simultaneously enhances data security and market efficiency. These findings provide empirically grounded, policy-actionable insights and a verifiable simulation framework to inform the evolution of bidirectional traceability regulations.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

This paper makes the opaque data market in the AI economy empirically legible for the first time, constructing a computational testbed to address a core epistemic failure: regulators governing a market defined by structural opacity, fragile price discovery, and brittle technical safeguards that have paralyzed traditional empirics and fragmented policy. The pipeline begins with multi-year fieldwork to extract the market's hidden logic, and then embeds these grounded behaviors into a high-fidelity ABM, parameterized via a novel LLM-based discrete-choice experiment that captures the preferences of unsurveyable populations. The pipeline is validated against reality, reproducing observed trade patterns. This policy laboratory delivers clear, counter-intuitive results. First, property-style relief is a false promise: ''anonymous-data'' carve-outs expand trade but ignore risk, causing aggregate welfare to collapse once external harms are priced in. Second, social welfare peaks when the downstream buyer internalizes the full substantive risk. This least-cost avoider approach induces efficient safeguards, simultaneously raising welfare and sustaining trade, and provides a robust empirical foundation for the legal drift toward two-sided reachability. The contribution is a reproducible pipeline designed to end the reliance on intuition. It converts qualitative insight into testable, comparative policy experiments, obsoleting armchair conjecture by replacing it with controlled evidence on how legal rules actually shift risk and surplus. This is the forward-looking engine that moves the field from competing intuitions to direct, computational analysis.

Problem

• Making opaque AI data markets empirically legible through computational testbeds
• Addressing structural opacity and fragile safeguards in data governance
• Replacing intuitive policy conjecture with evidence-based computational analysis

Innovation

• Computational testbed models opaque AI data markets
• LLM-based discrete-choice experiment captures hidden preferences
• Pipeline converts qualitative insights into testable policy experiments