The value and copyright of training data are crucial in the artificial intelligence industry. Service platforms should protect data providers' legitimate rights and fairly reward them for their contributions. Shapley value, a potent tool for evaluating contributions, outperforms other methods in theory, but its computational overhead escalates exponentially with the number of data providers. Recent works based on Shapley values attempt to mitigate computation complexity by approximation algorithms. However, they need to retrain for each test sample, leading to intolerable costs. We propose Fast-DataShapley, a one-pass training method that leverages the weighted least squares characterization of the Shapley value to train a reusable explainer model with real-time reasoning speed. Given new test samples, no retraining is required to calculate the Shapley values of the training data. Additionally, we propose three methods with theoretical guarantees to reduce training overhead from two aspects: the approximate calculation of the utility function and the group calculation of the training data. We analyze time complexity to show the efficiency of our methods. The experimental evaluations on various image datasets demonstrate superior performance and efficiency compared to baselines. Specifically, the performance is improved to more than 2 times, and the explainer's training speed can be increased by two orders of magnitude.

翻译：训练数据的价值与版权在人工智能产业中至关重要。服务平台应保护数据提供者的合法权益，并基于其贡献给予公平回报。Shapley值作为一种评估贡献的有力工具，在理论上优于其他方法，但其计算开销随数据提供者数量呈指数级增长。近期基于Shapley值的研究试图通过近似算法降低计算复杂度，但这类方法需为每个测试样本重新训练模型，导致难以承受的成本。本文提出Fast-DataShapley，一种单次训练方法：通过利用Shapley值的加权最小二乘特性，训练具有实时推理速度的可复用解释器模型。对于新测试样本，无需重新训练即可计算训练数据的Shapley值。此外，我们提出三种具有理论保证的方法，从效用函数近似计算与训练数据分组计算两方面降低训练开销。通过时间复杂度分析验证了方法的效率。在多类图像数据集上的实验评估表明，相较于基线方法，本方法在性能与效率上均表现出显著优势：性能提升至2倍以上，解释器训练速度可提高两个数量级。