VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI
<|reference_start|>VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI: 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.<|reference_end|>
@article{das2024vtrust:,
    title={VTruST: Controllable value function based subset selection for
  Data-Centric Trustworthy AI},
    author={Soumi Das, Shubhadip Nag, Shreyyash Sharma, Suparna Bhattacharya,
  Sourangshu Bhattacharya},
    journal={arXiv preprint arXiv:2403.05174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05174},
    primaryClass={cs.LG}
}