Incentivizing Truthful Data Sharing in Collaborative Machine Learning and Data Marketplaces

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based solely on the quantity of submitted data are vulnerable to manipulation, as agents may submit fabricated or low-quality data to inflate their rewards. Existing approaches for incentivizing truthful data contributions typically rely on strong assumptions about the underlying data distribution or restrictive models of strategic behavior, limiting their practical applicability.

In this talk, I will present our recent work addressing these challenges. I will begin with our approach to incentivizing truthful data contributions in normal mean estimation problems. I will then describe a more general reward mechanism built on a novel two-sample test inspired by the Cramér–von Mises statistic. We show that truthful reporting constitutes a Nash equilibrium under our framework. I will also briefly highlight some empirical results on real-world language and image datasets demonstrating that our methods effectively promote truthful data sharing.

Relevant papers:
- Chen et al 2023: Mechanism Design for Collaborative Normal Mean Estimation
- Clinton et al 2025, Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution
- Clinton et al 2025, A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing