VTruST: Controllable value function based subset selection for
  Data-Centric Trustworthy AI
Soumi Das Shubhadip Nag Shreyyash Sharma Suparna Bhattacharya Sourangshu Bhattacharya
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.