Semivalue-based data valuation is arbitrary and gameable

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.