NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty

TL;DR

This paper introduces NH-CROP, a robust online pricing framework for language data assets that adaptively acquires cost information to optimize revenue under uncertainty.

Contribution

It proposes a novel clipped robust pricing method with an information gate, improving decision-making in governed language data markets under cost uncertainty.

Findings

Clipped NH-CROP variants outperform or match baseline methods across benchmarks.

Paid verification is often not the main contributor to improved performance.

Refined cost information has significant local value even when verification is not always used.

Abstract

Language data are increasingly acquired and governed as assets, yet platforms often price candidate resources before knowing their true privacy or access costs. We study online pricing for governed language data assets under cost uncertainty. At each round, a platform observes an NLP task, a candidate asset, and a coarse cost estimate, may pay for a refined cost signal, posts a price, and receives safe net revenue. We introduce \textsc{NH-CROP}, a clipped robust pricing framework with a no-harm information-acquisition gate. The method compares direct pricing, risk-aware pricing, and verify-then-price, and acquires information only when its estimated decision value exceeds the best no-verification alternative. Across synthetic, real-proxy, and downstream-utility-grounded benchmarks, clipped \textsc{NH-CROP} variants improve or remain competitive with price-only and risk-aware baselines. Causal ablations show that paid verification is not the main source of gains in real-proxy and utility-grounded settings: the strongest learned policies often choose not to verify. Oracle and high-decision-value diagnostics show that refined cost information can still have substantial local value. Overall, governed language-data platforms should calibrate pricing under uncertain access costs first and verify only when information is cheap and decision-actionable.