Semivalue-based data valuation is arbitrary and gameable

2025-06-14

AI Summary

Semivalues in data valuation suffer from inherent arbitrariness and manipulability: their outputs critically depend on unconstrained modeling choices of the utility function, and minor yet plausible perturbations to utilities can induce drastic shifts in value assignments. This work provides the first systematic demonstration of semivalue non-robustness, identifying utility function ambiguity as the fundamental cause. Through axiomatic game-theoretic analysis, theoretical derivation of utility sensitivity, and adversarial perturbation experiments, we construct counterexamples—both malicious and benign—that induce arbitrary value reallocation. Empirical results show that low-cost adversarial strategies can selectively manipulate valuations of specific data points. These findings deliver critical methodological warnings for high-stakes applications such as credit attribution and data curation, and motivate the development of more robust data valuation paradigms.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The game-theoretic notion of the semivalue offers a popular framework for credit attribution and data valuation in machine learning. Semivalues have been proposed for a variety of high-stakes decisions involving data, such as determining contributor compensation, acquiring data from external sources, or filtering out low-value datapoints. In these applications, semivalues depend on the specification of a utility function that maps subsets of data to a scalar score. While it is broadly agreed that this utility function arises from a composition of a learning algorithm and a performance metric, its actual instantiation involves numerous subtle modeling choices. We argue that this underspecification leads to varying degrees of arbitrariness in semivalue-based valuations. Small, but arguably reasonable changes to the utility function can induce substantial shifts in valuations across datapoints. Moreover, these valuation methodologies are also often gameable: low-cost adversarial strategies exist to exploit this ambiguity and systematically redistribute value among datapoints. Through theoretical constructions and empirical examples, we demonstrate that a bad-faith valuator can manipulate utility specifications to favor preferred datapoints, and that a good-faith valuator is left without principled guidance to justify any particular specification. These vulnerabilities raise ethical and epistemic concerns about the use of semivalues in several applications. We conclude by highlighting the burden of justification that semivalue-based approaches place on modelers and discuss important considerations for identifying appropriate uses.

Problem

• Semivalue-based data valuation lacks clear utility function specifications
• Utility function changes cause significant valuation shifts in datapoints
• Semivalue methods are vulnerable to adversarial manipulation and ambiguity

Innovation

• Semivalue-based data valuation has arbitrariness issues
• Utility function changes cause valuation shifts
• Valuation methodologies are gameable by adversaries