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.

提出了一种基于数据的可控信任 AI（DCTAI）框架 VTruST，该框架允许用户控制构建训练数据集时信任度指标之间的权衡，通过提出一种在线值函数为基础的训练数据子集选择算法来实现高效的 DCTAI 框架。实验结果表明，VTruST 在社交、图像和科学数据集上优于现有基线算法，并且 VTruST 生成的数据值可为不同信任度指标提供有效的数据中心解释。