Machine Learning Model Trading With Verification Under Information Asymmetry

2025-10-01

IEEE Transactions on Networking

AI Summary

This study addresses the problem of seller performance misrepresentation in machine learning model markets caused by information asymmetry. The authors propose a game-theoretic framework that integrates verification mechanisms with heterogeneous buyer strategies. Through systematic analysis of how verification accuracy and cost influence sellers’ incentives to cheat, the work demonstrates— for the first time—that reducing information asymmetry can yield mutual gains for both buyers and sellers. Counterintuitively, the study also finds that preserving buyer order privacy does not enhance overall market welfare. By quantifying the deterrent effect of verification on fraudulent behavior, the research derives optimal pricing and verification strategies that jointly maximize market efficiency and trustworthiness, thereby establishing a theoretical foundation and design paradigm for trustworthy ML model trading.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Machine Learning: Privacy

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Machine learning (ML) model trading, known for its role in protecting data privacy, faces a major challenge: information asymmetry. This issue can lead to model deception, a problem that current literature has not fully solved, where the seller misrepresents model performance to earn more. We propose a game-theoretic approach, adding a verification step in the ML model market that lets buyers check model quality before buying. However, this method can be expensive and offers imperfect information, making it harder for buyers to decide. Our analysis reveals that a seller might probabilistically conduct model deception considering the chance of model verification. This deception probability decreases with the verification accuracy and increases with the verification cost. To maximize seller payoff, we further design optimal pricing schemes accounting for heterogeneous buyers’ strategic behaviors. Interestingly, we find that reducing information asymmetry benefits both the seller and buyer. Meanwhile, protecting buyer order information doesn’t improve the payoff for the buyer or the seller. These findings highlight the importance of reducing information asymmetry in ML model trading and open new directions for future research.

Problem

• machine learning model trading
• information asymmetry
• model deception
• verification
• game theory

Innovation

• information asymmetry
• model verification
• game-theoretic approach
• optimal pricing
• model deception