Towards Efficient Spark-Enabled Computation of Shapley Values
João P. Marques-Silva, Ramón Béjar, Jordi Planes · Frontiers in artificial intelligence and applications · 2025
Shapley values find a growing number of uses in different fields of knowledge, including machine learning, game theory, but also analysis of inconsistency, analysis and query answering in databases, among others. The complexity of computing Shapley values is most often unwieldy. As a result, approximate methods have been devised in the past. Some of the proposed methods are amenable to large-scale parallelization. This paper investigates the use of Spark for accelerating the approximate computation of Shapley values, namely in settings where this computation is extremely time-consuming.