Faithful Group Shapley Value
Kiljae Lee, Ziqi Liu, Weijing Tang, Yuan Zhang
Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models.  In practice, group-level data valuation is desirable when data providers contribute data in batch.  However, we identify that existing group-level extensions of Data Shapley are vulnerable to \emph{shell company attacks}, where strategic group splitting can unfairly inflate valuations.  We propose Faithful Group Shapley Value (FGSV) that uniquely defends against such attacks.  Building on original mathematical insights, we develop a provably fast and accurate approximation algorithm for computing FGSV.  Empirical experiments demonstrate that our algorithm significantly outperforms state-of-the-art methods in computational efficiency and approximation accuracy, while ensuring faithful group-level valuation.