Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with fairness, robustness, and accuracy being some of the key trustworthiness metrics. In this work, we propose a controllable framework for data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the trade-offs between the different trustworthiness metrics of the constructed training datasets. A key challenge in implementing an efficient DCTAI framework is to design an online value-function-based training data subset selection algorithm. We pose the training data valuation and subset selection problem as an online sparse approximation formulation. We propose a novel online version of the Orthogonal Matching Pursuit (OMP) algorithm for solving this problem. Experimental results show that VTruST outperforms the state-of-the-art baselines on social, image, and scientific datasets. We also show that the data values generated by VTruST can provide effective data-centric explanations for different trustworthiness metrics.

翻译：可信AI对于在公平性、鲁棒性和准确性等关键可信度指标的高风险应用中广泛采用AI至关重要。本文提出了一种面向数据为中心的可靠AI（DCTAI）的可控框架——VTruST，该框架允许用户控制所构建训练数据集在不同可信度指标之间的权衡。实现高效DCTAI框架的关键挑战在于设计一种基于在线价值函数的训练数据子集选择算法。我们将训练数据估值与子集选择问题建模为在线稀疏近似公式，并提出一种新颖的在线版本正交匹配追踪（OMP）算法来求解该问题。实验结果表明，VTruST在社交、图像和科学数据集上均优于现有最优基线方法。我们还证明VTruST生成的数据值可为不同可信度指标提供有效的以数据为中心的解释。