Understanding the decision-making process of machine learning models is crucial for ensuring trustworthy machine learning. Data Shapley, a landmark study on data valuation, has significantly advanced this understanding by assessing the contribution of each datum to model accuracy. However, the resource-intensive and time-consuming nature of multiple model retraining poses significant challenges for applying Data Shapley to large datasets. To address this, we propose the CHG (Conduct of Hardness and Gradient) score, which approximates the utility of each data subset on model accuracy during a single model training. By deriving the closed-form expression of the Shapley value for each data point under the CHG score utility function, we reduce the computational complexity to the equivalent of a single model retraining, an exponential improvement over existing methods. Additionally, we employ CHG Shapley for real-time data selection, demonstrating its effectiveness in identifying high-value and noisy data. CHG Shapley facilitates trustworthy model training through efficient data valuation, introducing a novel data-centric perspective on trustworthy machine learning.

翻译：理解机器学习模型的决策过程对于确保可信机器学习至关重要。Data Shapley作为数据价值评估领域的里程碑研究，通过评估每个数据点对模型精度的贡献，显著推进了这一理解。然而，多次模型重训练所需的资源密集与耗时特性，给Data Shapley在大规模数据集上的应用带来了重大挑战。为此，我们提出CHG（难度与梯度行为）评分，该评分可在单次模型训练过程中近似评估每个数据子集对模型精度的效用。通过推导每个数据点在CHG评分效用函数下Shapley值的闭式表达式，我们将计算复杂度降低至相当于单次模型重训练的水平，较现有方法实现了指数级改进。此外，我们采用CHG Shapley进行实时数据选择，证明了其在识别高价值与噪声数据方面的有效性。CHG Shapley通过高效的数据价值评估促进了可信模型训练，为可信机器学习引入了以数据为中心的新视角。