From Fairness to Truthfulness: Rethinking Data Valuation Design

Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy

Keywords: Data Valuation, Data Market, Data Pricing, Truthful Mechanism Design

Abstract:

As large language models increasingly rely on external data sources, fairly com-
pensating data contributors has become a central concern. In this paper, we revisit
the design of data markets through a game-theoretic lens, where data owners face
private, heterogeneous costs for data sharing. We show that commonly used valu-
ation methods—such as Leave-One-Out and Data Shapley—fail to ensure truthful
reporting of these costs, leading to inefficient market outcomes. To address this,
we adapt well-established payment rules from mechanism design, namely Myer-
son and Vickrey-Clarke-Groves (VCG), to the data market setting. We demon-
strate that the Myerson payment is the minimal truthful payment mechanism, op-
timal from the buyer’s perspective, and that VCG and Myerson payments coincide
in unconstrained allocation settings. Our findings highlight the importance of in-
corporating incentive compatibility into data valuation, paving the way for more
robust and efficient data markets.

Submission Number: 86