This research examines how we can design truthful data markets that fairly compensate data contributors for large language models.

• Traditional valuation methods (Leave-One-Out, Data Shapley) fail to ensure honest cost reporting
• Game-theoretic analysis reveals fundamental inefficiencies in current market designs
• Authors propose alternative mechanisms for truthful cost revelation
• Implications directly impact data security and fair compensation strategies

For security professionals, this work highlights the importance of incentive-compatible data sharing frameworks that protect both contributors and the integrity of AI training processes.

From Fairness to Truthfulness: Rethinking Data Valuation Design